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REVIEW 4 major objections 6 minor 60 references

Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Block Modulated Imaging compresses satellite imagery in one masked exposure and decodes it with quality matching single-pixel imaging.

desk verdict Solid engineering paper with a plausible idea, but the central on-orbit performance claim rests on simulation while the hardware prototype itself shows the model is violated in practice. read the letter →

arxiv 2412.18417 v2 pith:D2DXP5YQ submitted 2024-12-24 eess.IV cs.CV

Zhibin Wang , Yanxin Cai , Jiayi Zhou , Yangming Zhang , Tianyu Li , Wei Li , Xun Liu , Guoqing Wang
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Yang Yang
This is my paper · ORCID
classification eess.IVcs.CV
keywords blockmodulatedimagingcompressedsensingremoteimagecompressiondeepunfoldingnetworksingle-pixelcomputationalopticalmodulationon-orbitprocessing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to establish that a satellite can compress an image almost for free at acquisition time by passing light through a fixed binary photomask and summing the masked image into blocks, with a single exposure and no digital micromirror device. The authors claim this Block Modulated Imaging (BMI) scheme matches or beats single-pixel imaging reconstruction quality on remote sensing benchmarks while requiring far less encoding computation and fewer exposures. To decode the resulting compressed measurement, they introduce BMNet, a deep unfolding network that reconstructs the image using gated 3D convolutions and cross-stage attention. If the claims hold, on-orbit compression for high-resolution remote sensing becomes a simple optical step instead of a heavy digital-processing task, and the paper also reports a working hardware prototype supporting this picture.

What carries the argument

The load-bearing object is the encoding equation y = Φx (Eq. 3): after element-wise multiplication of the scene X by a binary mask M, the modulated image is partitioned into N blocks and summed into a single measurement Y, so each measurement is the sum of one masked block. Decoding is posed as the ill-posed inverse problem of Eq. (4) and solved by a deep unfolding network BMNet that alternates a linear projection step v^(k) = x^(k−1) + Φ^T(ΦΦ^T + η^(k))^(−1)(y − Φx^(k−1)) with a learned denoiser D^(k) built from gated 3D convolutions applied to the stacked image blocks. The Two-Way Cross-Attention module exchanges latent information between unfolding stages to counter information loss, and a lightweight 2D U-net refines the final output to suppress block artifacts.

What would settle it

Set up the BMI optics in a laboratory with a known scene and a calibrated mask, then compare the measured block-summed intensities with the predicted Φx. If the per-block residual exceeds the sensor noise floor by a significant margin (or if BMNet's reconstructed PSNR on such real measurements falls more than a few dB below its simulation performance without fine-tuning), the linear model underpinning the compression claim fails in practice.

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Extended reading notes

Core claim

The central claim is that block-wise optical modulation followed by block summation constitutes a practical compressed-sensing encoder for remote sensing images, with the linear model y = Φx where each block of the measurement is the sum of a masked image block, and that a deep unfolding decoder can invert it. BMI encodes the whole image in one exposure, removing the time-series acquisition and digital micromirror device requirements of single-pixel imaging, and BMNet reconstructs images with PSNR/SSIM comparable to or better than the state-of-the-art SPI decoder SAUNet at compression ratios 4, 10 and 25 on CBSD68 and DOTA-v1.0, while running 2–4 times faster at resolutions from 256 to 1024. The paper further claims downstream object detection and semantic segmentation degrade by at most 7.5% for compression ratios below 16, that the same network trained on visible light generalizes to a SWIR band, and that a prototype camera encodes real scenes with the same measurement pipeline.

Load-bearing premise

The load-bearing premise is that the real optical system behaves like the ideal linear model y = Φx with a known binary mask, including the block-sum operation on the sensor; the prototype results in the paper show visible deviations from simulation, so if uncalibrated optical effects persist after fine-tuning with the measured mask, the claimed compression performance would not transfer to orbit.

Editorial extensions

If this is right

  • Satellites can compress images during acquisition: encoding is a fixed optical mask plus block summation, with measured encoding time growing from 0.03 ms at 512x512 to 14.46 ms at 8192x8192, far below JPEG's times.
  • Single-exposure acquisition removes the static-scene requirement that hinders single-pixel imaging on moving platforms, and eliminates DMD limitations on resolution.
  • The same trained decoder transfers across datasets and even to a SWIR band without fine-tuning, suggesting the compression format is agnostic to the spectral content tested.
  • Downstream tasks inherit predictable quality: object detection and semantic segmentation lose at most 7.5% on their metrics for compression ratios up to 16, giving system designers a compression budget.
  • Decoding is 2–4 times faster than the compared SPI network, making ground-based reconstruction of high-resolution imagery more practical.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An extension the authors leave implicit: because the compression ratio is fixed by the block count N, the binary mask can be co-optimized with the decoder for a specific spectral band or downstream task, which the current experiments do not attempt.
  • The prototype still uses a DMD for configurability; replacing it with a fixed photomask in a flight design would remove the 45-degree rotation and calibration mismatch seen in Section IV-F, a testable step the paper does not build.
  • The block-sum encoding suggests a direct video extension: applying BMI frame-by-frame would yield a single-shot compressive video codec, though the lack of guaranteed inter-block correlation would require a decoder that models motion.
  • If encoding is indeed near-zero cost, the practical trade-off shifts entirely to decoder quality and bit rate; task-driven training of the mask could push BMI closer to JPEG-style codecs at low compression ratios, a comparison the paper only begins.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript proposes Block Modulated Imaging (BMI), an optical-domain compressed sensing scheme for remote sensing image compression. A scene is multiplied by a binary mask, the modulated image is partitioned and summed on the sensor, and a deep-unfolding network (BMNet) with gated 3D convolutions and two-way cross-attention reconstructs the image from the resulting measurement. Experiments compare BMNet with SPI decoders on CBSD68 and DOTA-v1.0, evaluate downstream object detection and semantic segmentation, test generalization to SWIR Landsat8 data, report decoding and encoding speeds, and describe a hardware prototype.

Significance. If the simulation results transfer to hardware, the contribution is significant: BMI would provide single-exposure compressive acquisition without DMDs, ultra-low encoding cost, and competitive reconstruction quality. The code release, the multi-dataset evaluation, the downstream-task analysis, and the hardware prototype are genuine strengths. However, the hardware validation is currently qualitative, the SPI comparison is partly confounded, and the mask construction is underspecified, so the practical on-orbit claim is not yet fully supported.

major comments (4)
  1. [Section IV-F, Fig. 7] The real-world prototype study does not quantitatively validate the central practical claim. No PSNR or SSIM values are reported for the hardware reconstructions, and the text states that the calibrated mask deviates from the binary encoding used in simulation, that the model had to be fine-tuned with the measured mask, and that reconstructions still differ from simulation because of optical field fluctuations, relay-lens imperfections, and system errors. Since the decoder's linear projection (Eq. 5) uses the assumed sensing operator, this leaves open the possibility that the quality levels reported in Table I and Fig. 5 do not transfer to a deployed system. Please add quantitative evaluation with the measured mask, including PSNR/SSIM on a held-out set of scenes and a comparison against simulation on the same scenes, or explicitly restrict the central performance claims to simulation and describe the prototype as a qualitative feasibility demonstration.
  2. [Section IV-F] The prototype does not exercise two of the three claimed advantages over SPI. It retains a DMD (ViALUX V-9001) rather than a photomask, and the reconstruction is performed at 512x512, which is below the high-resolution regime that the paper motivates for DMD-free operation. The sentence stating that the DMD can be substituted with a photomask in practical applications is an assertion, not a demonstration. Please provide a photomask-based or mask-calibrated high-resolution experiment, or soften the contribution statement accordingly.
  3. [Section IV-C, Table I] The comparison against SPI decoders is partly confounded. For CBSD68, the text says that the model adopted a jointly-trained measurement matrix, but the BMI encoding defined in Section III-A is a binary mask followed by block summation; a jointly-trained real-valued matrix is a different sensing operator, and it is not stated whether it remains binary and block-structured or is optically realizable. On DOTA-v1.0, only SAUNet is used as the SPI baseline, and at Cr=4 BMNet is actually lower than SAUNet (43.23 dB versus 44.19 dB), so the statement that BMNet is comparable to or exceeds SOTA SPI is not uniformly supported. Please report the mask constraints, clarify whether the learned matrix is used for both datasets, and add at least one additional SPI baseline on DOTA or justify why SAUNet alone is representative.
  4. [Section III-A, IV-B] The construction and training of the mask M are underspecified. The paper does not state how M is generated (e.g., random Bernoulli, learned, fixed per dataset), how binary constraints are enforced, how the block partition size N is related to the reported compression ratios, or whether the same mask is used across images and datasets. Because the measurement operator is the defining component of BMI, and because a jointly-trained matrix is invoked for CBSD68, these details are necessary to reproduce Table I and to assess whether the encoding is physically implementable. Please provide a precise mask-generation procedure, including any thresholding or binarization, and state the block partition geometry.
minor comments (6)
  1. [Section III-A] The term 'Hardman product' in Eq. (1) should be 'Hadamard product'.
  2. [Section IV-A] The heading 'Datesets and Metrics' contains a typo; it should be 'Datasets and Metrics'.
  3. [Section IV-H2, Table V] Please specify what exactly is timed for BMI in Table V; 0.03 ms for a 512x512 image at Cr=16 appears to include only the block summation, not the optical modulation or sensor readout, and the JPEG timing conditions should be stated for a fair comparison.
  4. [References] Reference [26] is cited as 'BMVC' in the text but is listed as an arXiv preprint; please update the citation to the published venue if it has appeared.
  5. [Table III] In the ablation table, the first three rows have empty entries in the 'Gated Conv.' and 'TWCA' columns; use explicit check marks or crosses for all rows to avoid ambiguity about which module is ablated.
  6. [Section IV-C, Fig. 5] The numerical comparisons in Table I and Fig. 5 are reported as single-run averages without error bars or significance information; given that some gaps between methods are only a few tenths of a decibel, a sentence on repeatability would strengthen the comparison.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the BMI encoding model and BMNet decoding are evaluated on external held-out benchmarks, with only a minor prior-work lineage citation that is not load-bearing.

full rationale

The paper's derivation chain is self-contained against external evidence. The BMI encoding model is defined in Eqs. (1)-(3) and used to simulate measurements; the decoder (Eqs. (4)-(12)) is trained on DOTA-v1.0 training patches and evaluated on held-out DOTA-v1.0 test patches as well as CBSD68, Vaihingen, and Landsat8, none of which are used for training. The comparisons against SPI decoders (SAUNet et al.) are therefore genuine empirical benchmarks, not fitted outputs relabeled as predictions. Section IV-F explicitly acknowledges that the real prototype deviates from the ideal binary-mask model (calibrated masks deviate from the binary encoding, reconstruction results exhibit discrepancies, and sensor response is left outside scope), which is a limitation on the deployment claim but does not make the simulation evaluation circular. The encoding is explicitly credited to prior block-modulating work [26]; this citation is an inspiration/prior-art reference rather than a load-bearing proof, because the encoding equations and the experimental evaluation stand on their own. No self-citation chain or definitional identity forces the reported results. The score of 1 reflects the minor lineage to prior block-modulation work and the jointly trained measurement matrix used in the CBSD68 comparison, neither of which is load-bearing or circular.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The BMI system rests on four main assumptions: the applicability of CS theory to a block-diagonal measurement operator, the learnability of a natural-image prior, the stability of the GAP unfolding, and the exact knowledge of the mask. The only fitted numbers are the network weights, the mask (when learned), per-stage regularizers, and standard hyperparameters; there are no invented physical entities.

free parameters (4)
  • BMNet network weights = not reported (millions of parameters)
    All parameters of the 10-stage gated-3D-conv TWCA network and the refinement 2D U-Net are learned on the DOTA training set; reported PSNR is an outcome of these fitted values.
  • Binary mask M = not reported
    The paper states a jointly-trained measurement matrix was used for CBSD68 but does not describe how the mask is generated or trained for the main DOTA experiments; if trained, the H×W binary matrix is a massive free parameter.
  • Per-stage regularizer η^(k) = learned, values not reported
    Added to the diagonal of ΦΦ^T in Eq. (5) for each of the 10 stages, these scalars are optimized during training.
  • Hyperparameters (stages=10, feature dims {32,64,128,64,32}, learning rates) = as listed in Section IV-B
    Chosen by hand; standard for deep unfolding networks, but they affect the results.
assumptions (4)
  • domain assumption Standard compressed sensing theory applies to the BMI sensing matrix, justifying the claim that high-quality reconstruction is possible from fewer measurements.
    Invoked in Section III-A ('The 0-1 mask can be viewed as a sparse representation, aligning with the principles of compressed sensing'), but no RIP, null-space, or other CS guarantee is established for the block-diagonal measurement operator.
  • domain assumption Natural remote sensing images have strong priors that can be learned by a 3D U-Net, so the unfolded network can act as a powerful denoiser/prior.
    The decoding quality relies entirely on the learned prior; the paper provides no analysis of when such learned inversion succeeds.
  • domain assumption The GAP update (Eq. 5) provides a valid sequence of auxiliary variables when unfolded with learned regularizers.
    Borrowed from GAP [42] and SCI unfolding [37]; no convergence proof is provided for the learned, non-convex setting.
  • domain assumption The binary mask M is exactly known at the decoder and is used in the linear projection.
    Eq. (3) and the unfolding require exact knowledge of Φ; in the prototype, the mask is measured and used for fine-tuning, implying the mask can be calibrated.

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Cite this review

Pith. "Pith review of Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging." pith.science (2026). https://pith.science/paper/D2DXP5YQ

@misc{pith2026241218417,
  author       = {Pith},
  title        = {Pith review of: Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D2DXP5YQ}},
  note         = {Machine review of arXiv:2412.18417}
}
read the original abstract

The growing field of remote sensing faces a challenge: the ever-increasing size and volume of imagery data are exceeding the storage and transmission capabilities of satellite platforms. Efficient compression of remote sensing imagery is a critical solution to alleviate these burdens on satellites. However, existing compression methods are often too computationally expensive for satellites. With the continued advancement of compressed sensing theory, single-pixel imaging emerges as a powerful tool that brings new possibilities for on-orbit image compression. However, it still suffers from prolonged imaging times and the inability to perform high-resolution imaging, hindering its practical application. This paper advances the study of compressed sensing in remote sensing image compression, proposing Block Modulated Imaging (BMI). By requiring only a single exposure, BMI significantly enhances imaging acquisition speeds. Additionally, BMI obviates the need for digital micromirror devices and surpasses limitations in image resolution. Furthermore, we propose a novel decoding network specifically designed to reconstruct images compressed under the BMI framework. Leveraging the gated 3D convolutions and promoting efficient information flow across stages through a Two-Way Cross-Attention module, our decoding network exhibits demonstrably superior reconstruction performance. Extensive experiments conducted on multiple renowned remote sensing datasets unequivocally demonstrate the efficacy of our proposed method. To further validate its practical applicability, we developed and tested a prototype of the BMI-based camera, which has shown promising potential for on-orbit image compression. The code is available at https://github.com/Johnathan218/BMNet.

Figures

Figures reproduced from arXiv: 2412.18417 by the authors.

Figure 1
Figure 1. Satellite-side optical encoding for compression and ground-based deep unfolding decoding for reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) BMNet processes the measurement Y alongside the mask M, and reconstructs the original signal X. v (k) denotes the auxiliary variable at the k-th stage (see Eq. 5). (b) The proposed gated 3D convolution block. (c) The proposed Two-Way Cross-Attention module. u (k) denotes the latent variable generated by the encoder of the k-th 3D U-net. h (k) h denotes the hidden variable of the k-th stage (h (0) = u (0)). regar… view at source ↗
Figure 3
Figure 3. Visualization of the reconstructed images on DOTA-v1.0 at different [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Comprehensive performance of BMNet under varying compression ratios (Cr): PSNR, SSIM for image reconstruction (DOTA-v1.0), mAP for object [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Visualization of the reconstructed SWIR band at different compression [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Real-world testing of the prototype. (a) The digital acquisition unit. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Comparison of masks in different environments. (a) Simulated [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Decoding performance of JPEG and BMI at 512x512 resolution under [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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Reference graph

Works this paper leans on

60 extracted references · 48 canonical work pages

  1. [1]

    Inclusive consistency- based quantitative decision-making framework for incremental automatic target recognition,

    S. Dang, Z. Xia, X. Jiang, S. Gui, and X. Feng, “Inclusive consistency- based quantitative decision-making framework for incremental automatic target recognition,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–14, 2023

    work page 2023

  2. [2]

    Utilization of aspect angle infor- mation in synthetic aperture images,

    D. S. Plotnick and T. M. Marston, “Utilization of aspect angle infor- mation in synthetic aperture images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 9, pp. 5424–5432, 2018

    work page 2018

  3. [3]

    Satellite image analysis for disaster and crisis-management support,

    S. V oigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl, “Satellite image analysis for disaster and crisis-management support,” IEEE Transactions on Geoscience and Remote Sensing , vol. 45, no. 6, pp. 1520–1528, 2007

    work page 2007

  4. [4]

    Big data for remote sensing: Challenges and opportunities,

    M. Chi, A. Plaza, J. A. Benediktsson, Z. Sun, J. Shen, and Y . Zhu, “Big data for remote sensing: Challenges and opportunities,” Proceedings of the IEEE, vol. 104, no. 11, pp. 2207–2219, 2016

    work page 2016

  5. [5]

    Key issues on application of remote sens- ing big data (rsbd): Concepts, scenarios, and challenges,

    S. Shi, H. Wang, and X. Ma, “Key issues on application of remote sens- ing big data (rsbd): Concepts, scenarios, and challenges,” in 2023 2nd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) , 2023, pp. 238–245

    work page 2023

  6. [6]

    Remote- sensing image scene classification with deep neural networks in jpeg 2000 compressed domain,

    A. Preethy Byju, G. Sumbul, B. Demir, and L. Bruzzone, “Remote- sensing image scene classification with deep neural networks in jpeg 2000 compressed domain,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 3458–3472, 2021

    work page 2000

  7. [7]

    Comprehensive review on lossy and lossless compression techniques,

    S. Elakkiya and K. S. Thivya, “Comprehensive review on lossy and lossless compression techniques,” Journal of The Institution of Engineers (India): Series B , vol. 103, no. 3, pp. 1003–1012, 2022. [Online]. Available: https://doi.org/10.1007/s40031-021-00686-3

    work page doi:10.1007/s40031-021-00686-3 2022

  8. [8]

    Image compression techniques: A survey in lossless and lossy algorithms,

    A. Hussain, A. Al-Fayadh, and N. Radi, “Image compression techniques: A survey in lossless and lossy algorithms,” Neurocomputing, vol. 300, pp. 44–69, 2018. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S0925231218302935

    work page 2018

Show all 60 references
  1. [9]

    A highly efficient rate control algorithm for jpeg2000 images,

    X. Chen and X. Xu, “A highly efficient rate control algorithm for jpeg2000 images,” IEEE Transactions on Consumer Electronics, vol. 59, no. 3, pp. 587–591, 2013

    2013

  2. [10]

    Highly efficient, low complexity arithmetic coder for jpeg2000,

    F. Aul ´ı-Llin`as, “Highly efficient, low complexity arithmetic coder for jpeg2000,” in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 5601–5605

    2014

  3. [11]

    Low-complexity and efficient image coder/decoder with quad-tree search model for embedded computing platforms,

    T. Ma, “Low-complexity and efficient image coder/decoder with quad-tree search model for embedded computing platforms,” IET Image Processing, vol. 12, no. 2, pp. 235–242, 2018. [Online]. Available: https: //ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-ipr.2017.0469

    2018

  4. [12]

    A near lossless and low complexity image compression algorithm based on fixed threshold dpcm for capsule endoscopy,

    N. V . Malathkar and S. K. Soni, “A near lossless and low complexity image compression algorithm based on fixed threshold dpcm for capsule endoscopy,” Multimedia Tools and Applications , vol. 79, no. 11, pp. 8145–8160, 2020

    2020

  5. [13]

    Compressed sensing,

    D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006

    2006

  6. [14]

    Communication in the presence of noise,

    C. Shannon, “Communication in the presence of noise,” Proceedings of the IRE, vol. 37, no. 1, pp. 10–21, 1949

    1949

  7. [15]

    Single-pixel imaging via compressive sampling,

    M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 83–91, 2008

    2008

  8. [16]

    Single-pixel remote sensing,

    J. Ma, “Single-pixel remote sensing,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 2, pp. 199–203, 2009

    2009

  9. [17]

    A single-pixel imaging system for remote sensing by two-step iterative curvelet thresholding,

    ——, “A single-pixel imaging system for remote sensing by two-step iterative curvelet thresholding,” Geoscience and Remote Sensing Letters, IEEE, vol. 6, pp. 676 – 680, 11 2009

    2009

  10. [18]

    Single-pixel compressive imaging based on motion compensation,

    Z. Wang and J. Zhu, “Single-pixel compressive imaging based on motion compensation,” IET Image Processing , vol. 12, no. 12, pp. 2283–2291, 2018. [Online]. Available: https://ietresearch.onlinelibrary. wiley.com/doi/abs/10.1049/iet-ipr.2018.5741

    2018

  11. [19]

    Remote sensing image compression in visible/near-infrared range using heterogeneous compressive sensing,

    J. Li, Y . Fu, G. Li, and Z. Liu, “Remote sensing image compression in visible/near-infrared range using heterogeneous compressive sensing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 12, pp. 4932–4938, 2018

    2018

  12. [20]

    The jpeg still picture compression standard,

    G. K. Wallace, “The jpeg still picture compression standard,” Commun. ACM, vol. 34, no. 4, p. 30–44, apr 1991. [Online]. Available: https://doi.org/10.1145/103085.103089

    1991

  13. [21]

    The jpeg 2000 still im- age compression standard,

    A. Skodras, C. Christopoulos, and T. Ebrahimi, “The jpeg 2000 still im- age compression standard,” IEEE Signal Processing Magazine , vol. 18, no. 5, pp. 36–58, 2001

    2000

  14. [22]

    Three-dimensional single-pixel imaging with far fewer measurements than effective image pixels,

    Z. Zhang and J. Zhong, “Three-dimensional single-pixel imaging with far fewer measurements than effective image pixels,” Opt. Lett. , vol. 41, no. 11, pp. 2497–2500, Jun 2016. [Online]. Available: https://opg.optica.org/ol/abstract.cfm?URI=ol-41-11-2497

    2016

  15. [23]

    Single-pixel imaging of dynamic objects using multi-frame motion estimation,

    S. Monin, E. Hahamovich, and A. Rosenthal, “Single-pixel imaging of dynamic objects using multi-frame motion estimation,” Scientific Reports, vol. 11, no. 1, p. 7712, Apr 2021. [Online]. Available: https://doi.org/10.1038/s41598-021-83810-z

    2021 doi

  16. [24]

    New strategy for high-dimensional single-pixel imaging,

    X. Li, Y . Yin, W. He, X. Liu, Q. Tang, and X. Peng, “New strategy for high-dimensional single-pixel imaging,” Opt. Express , vol. 29, no. 22, pp. 36 675–36 688, Oct 2021. [Online]. Available: https://opg.optica.org/oe/abstract.cfm?URI=oe-29-22-36675

    2021

  17. [25]

    Single pixel imaging at high pixel resolutions,

    R. Stojek, A. Pastuszczak, P. Wr ´obel, and R. Koty ´nski, “Single pixel imaging at high pixel resolutions,” Opt. Express , vol. 30, no. 13, pp. 22 730–22 745, Jun 2022. [Online]. Available: https: //opg.optica.org/oe/abstract.cfm?URI=oe-30-13-22730

    2022

  18. [26]

    Block modulating video compression: an ultra low complexity image compression encoder for resource limited platforms,

    Y . Xue, S. Zheng, W. Tahir, Z. Wang, H. Zhang, Z. Meng, L. Tian, and X. Yuan, “Block modulating video compression: an ultra low complexity image compression encoder for resource limited platforms,” arXiv preprint arXiv:2205.03677 , 2022

    2022 arXiv

  19. [27]

    Plug-and-play algorithms for large- scale snapshot compressive imaging,

    X. Yuan, Y . Liu, J. Suo, and Q. Dai, “Plug-and-play algorithms for large- scale snapshot compressive imaging,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020

    2020

  20. [28]

    Gap-net for snapshot compressive imaging,

    Z. Meng, S. Jalali, and X. Yuan, “Gap-net for snapshot compressive imaging,” arXiv preprint arXiv:2012.08364 , 2020

    2012 arXiv

  21. [29]

    Remote sensing image compression: A review,

    S. Zhou, C. Deng, B. Zhao, Y . Xia, Q. Li, and Z. Chen, “Remote sensing image compression: A review,” in 2015 IEEE International Conference on Multimedia Big Data , April 2015, pp. 406–410

    2015

  22. [30]

    Constant snr, rate control, and entropy coding for predictive lossy hyperspectral image compres- sion,

    M. Conoscenti, R. Coppola, and E. Magli, “Constant snr, rate control, and entropy coding for predictive lossy hyperspectral image compres- sion,” IEEE Transactions on Geoscience and Remote Sensing , vol. 54, no. 12, pp. 7431–7441, 2016

    2016

  23. [31]

    A lightweight contextual arithmetic coder for on-board remote sensing data compression,

    J. Bartrina-Rapesta, I. Blanes, F. Aul ´ı-Llin`as, J. Serra-Sagrist `a, V . Sanchez, and M. W. Marcellin, “A lightweight contextual arithmetic coder for on-board remote sensing data compression,” IEEE Transac- tions on Geoscience and Remote Sensing , vol. 55, no. 8, pp. 4825–4...

    2017

  24. [32]

    Compressed sensing for image compression: Survey of algorithms,

    S. K. Gunasheela and H. S. Prasantha, “Compressed sensing for image compression: Survey of algorithms,” in Emerging Research in Comput- ing, Information, Communication and Applications , N. R. Shetty, L. M. Patnaik, H. C. Nagaraj, P. N. Hamsavath, and N. Nalini, Eds. Singapore...

    2019

  25. [33]

    Microwave single pixel imager (mspi) overview and imaging algo- rithm,

    J. Bobak, H. Alqadah, M. Nurnberger, S. Rudolph, and D. Truesdale, “Microwave single pixel imager (mspi) overview and imaging algo- rithm,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , 2019, pp. 8837–8840

    2019

  26. [34]

    Deep admm-net for compressive sensing mri,

    y. yang, J. Sun, H. Li, and Z. Xu, “Deep admm-net for compressive sensing mri,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016. [Online]. Available: https://proceedi...

    2016

  27. [35]

    Deep tensor admm-net for snapshot compressive imaging,

    J. Ma, X.-Y . Liu, Z. Shou, and X. Yuan, “Deep tensor admm-net for snapshot compressive imaging,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019

    2019

  28. [36]

    Snapshot compressive imaging: Theory, algorithms, and applications,

    X. Yuan, D. J. Brady, and A. K. Katsaggelos, “Snapshot compressive imaging: Theory, algorithms, and applications,” IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 65–88, 2021

    2021

  29. [37]

    Dense deep unfolding network with 3d-cnn prior for snapshot compressive imaging,

    Z. Wt, J. Zhangt, and C. Mou, “Dense deep unfolding network with 3d-cnn prior for snapshot compressive imaging,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . Los Alamitos, CA, USA: IEEE Computer Society, oct 2021, pp. 4872–4881. [Online]. Available: ht...

    2021

  30. [38]

    Unfolding framework with prior of convolution- transformer mixture and uncertainty estimation for video snapshot compressive imaging,

    S. Zheng and X. Yuan, “Unfolding framework with prior of convolution- transformer mixture and uncertainty estimation for video snapshot compressive imaging,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 12 692–12 703

    2023

  31. [39]

    Birnat: Bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging

    Z. Cheng, R. Lu, Z. Wang, H. Zhang, B. Chen, Z. Meng, and X. Yuan, “Birnat: Bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging.” Berlin, Heidelberg: Springer-Verlag, 2020, p. 258–275. [Online]. Available: https://doi.org/10...

    2020 doi

  32. [40]

    Memory-efficient network for large-scale video compressive sensing,

    Z. Cheng, B. Chen, G. Liu, H. Zhang, R. Lu, Z. Wang, and X. Yuan, “Memory-efficient network for large-scale video compressive sensing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 16 246–16 255

    2021

  33. [41]

    Distributed optimization and statistical learning via the alternating direction method of multipliers,

    S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 11 Learning, vol. 3, n...

    2021 doi

  34. [42]

    Generalized alternating projection for weighted- ℓ2,1 minimization with applications to model-based compressive sensing,

    X. Liao, H. Li, and L. Carin, “Generalized alternating projection for weighted- ℓ2,1 minimization with applications to model-based compressive sensing,” SIAM Journal on Imaging Sciences , vol. 7, no. 2, pp. 797–823, 2014. [Online]. Available: https://doi.org/10.1137/ 130936658

    2014

  35. [43]

    Free- form image inpainting with gated convolution,

    J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Free- form image inpainting with gated convolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019

    2019

  36. [44]

    Optimization-inspired cross-attention transformer for compressive sensing,

    J. Song, C. Mou, S. Wang, S. Ma, and J. Zhang, “Optimization-inspired cross-attention transformer for compressive sensing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 6174–6184

    2023

  37. [45]

    Memory-augmented deep unfolding network for compressive sensing,

    J. Song, B. Chen, and J. Zhang, “Memory-augmented deep unfolding network for compressive sensing,” in Proceedings of the 29th ACM International Conference on Multimedia , ser. MM ’21. New York, NY , USA: Association for Computing Machinery, 2021, p. 4249–4258. [Online]. Availa...

    2021

  38. [46]

    Restormer: Efficient transformer for high-resolution image restoration,

    S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), 2022, pp. 5718–5729

    2022

  39. [47]

    Dota: A large-scale dataset for object detection in aerial images,

    G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018

    2018

  40. [48]

    Saunet: Spatial-attention unfolding network for image compressive sensing,

    P. Wang and X. Yuan, “Saunet: Spatial-attention unfolding network for image compressive sensing,” in Proceedings of the 31st ACM International Conference on Multimedia , ser. MM ’23. New York, NY , USA: Association for Computing Machinery, 2023, p. 5099–5108. [Online]. Availab...

    2023

  41. [49]

    The isprs vaihingen dataset,

    ISPRS, “The isprs vaihingen dataset,” https://www.isprs.org/education/ benchmarks/UrbanSemLab/default.aspx, 2012, accessed: 2024-07-09

    2012

  42. [50]

    A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images,

    L. Wang, R. Li, C. Duan, C. Zhang, X. Meng, and S. Fang, “A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022

    2022

  43. [51]

    Landsat-8 dataset,

    U. G. Survey, “Landsat-8 dataset,” https://www.usgs.gov/ landsat-missions/landsat-8, 2013, accessed: 2024-07-09

    2013

  44. [52]

    Cbsd68 dataset,

    T. B. S. Dataset and Benchmark, “Cbsd68 dataset,” https://www2.eecs. berkeley.edu/Research/Projects/CS/vision/bsds/, accessed: 2024-07-09

    2024

  45. [53]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

    2014 arXiv

  46. [54]

    YOLOv5 by Ultralytics,

    G. Jocher, “YOLOv5 by Ultralytics,” May 2020. [Online]. Available: https://github.com/ultralytics/yolov5

    2020

  47. [55]

    Metacam,

    l. Peking Qing zhi yuan Shi technology co., “Metacam,” 2024, accessed: 2024-04-10. [Online]. Available: https://www.metacam.tech/

    2024

  48. [56]

    Fouriercam: a camera for video spectrum acquisition in a single shot,

    C. Hu, H. Huang, M. Chen, S. Yang, and H. Chen, “Fouriercam: a camera for video spectrum acquisition in a single shot,” Photon. Res., vol. 9, no. 5, pp. 701–713, May 2021. [Online]. Available: https://opg.optica.org/prj/abstract.cfm?URI=prj-9-5-701

    2021

  49. [57]

    Video object detection from one single image through opto- electronic neural network,

    ——, “Video object detection from one single image through opto- electronic neural network,” APL Photonics, vol. 6, no. 4, p. 046104, 04

  50. [58]

    Deep optics for video snapshot compressive imaging,

    P. Wang, L. Wang, and X. Yuan, “Deep optics for video snapshot compressive imaging,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 10 612–10 622

    2023

  51. [59]

    Opencv library,

    “Opencv library,” https://opencv.org/, accessed: 2024-12-01. VI. B IOGRAPHY SECTION Zhibin Wang Zhibin Wang received the B.E. de- gree from the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China, in 2023, where ...

    2024

  52. [2021]

    Available: https://doi.org/10.1063/5.0040424

    [Online]. Available: https://doi.org/10.1063/5.0040424

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.