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REVIEW 4 major objections 5 minor 74 references

Training-free Quantum-Inspired Image Edge Extraction Method

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The paper claims that a training-free pipeline of Sobel gradients, Schrödinger-inspired diffusion, and Canny-Laplacian fusion beats trained edge detectors on NYUD, BSD500, Multicue, and PASCAL.

desk verdict A training-free edge detector made of Sobel, heat-equation diffusion, and a Canny/Laplacian max-fusion whose benchmark scores are invalid because the final map is not thresholdable. read the letter →

arxiv 2501.18929 v1 pith:65BZKRBS submitted 2025-01-31 cs.CV

Arti Jain , Pradeep Singh This is my paper · ORCID
classification cs.CV MSC 68U1068T45
keywords edgedetectiontraining-freequantum-inspiredSchrödingerwaveequationCannydetectorLaplacianoperatorhybridbenchmarks
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

The paper sets out to show that a fully classical, training-free pipeline can beat deep neural edge detectors on standard benchmarks. The method combines a Sobel gradient map, an iterative diffusion step inspired by the Schrödinger wave equation, and a hybrid fusion of Canny and Laplacian edges. If the reported numbers hold, high-accuracy edge detection would no longer require large labeled datasets or GPU training, which matters for medical imaging, autonomous systems, and remote sensing. The paper further claims the full model runs in about 0.22 seconds per image on modest memory, making it deployable on constrained devices.

What carries the argument

The load-bearing mechanism is the Schrödinger-inspired evolution step, implemented as the discrete diffusion update $\psi(x,y,t+\Delta t)=\psi(x,y,t)+\delta\,\nabla^2\psi(x,y,t)$ with clipping to $[0,255]$, which smooths uniform regions while preserving and sharpening high-gradient edges. This refined image is then blurred with a Gaussian kernel and fed to two detectors: a Canny edge detector with thresholds $t_{\mathrm{low}}=50$ and $t_{\mathrm{high}}=150$, and a discrete Laplacian second-derivative response. The two edge maps are fused by the pointwise maximum $E_{\mathrm{hybrid}}(x,y)=\max(E_{\mathrm{canny}}(x,y), E_{\mathrm{lap}}(x,y))$, which is the step the paper credits for combining local and global edge evidence.

What would settle it

Re-implement the pipeline exactly as Eqs. (3)–(22) specify, run it on a sample of NYUD, BSD500, Multicue, and PASCAL images, and evaluate the raw $E_{\mathrm{hybrid}}$ map with the standard BSDS benchmark code; if the computed ODS/OIS/AP scores fall materially below the tables, or if the evaluation cannot handle the many tied values introduced by the binary Canny component, the central claim is refuted.

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Extended reading notes

Core claim

The central claim is that a hand-crafted edge detector, computed by refining Sobel gradients with an iterative Laplacian diffusion step modeled on the Schrödinger wave equation and then taking the pixel-wise maximum of a Canny binary map and a raw Laplacian response, outperforms state-of-the-art trained edge detectors. On NYUD the paper reports ODS 0.823, OIS 0.856, AP 0.889, and F-measure 0.896, against 0.774 ODS for the transformer-based EDTER baseline. On BSD500 the reported ODS is 0.856 versus 0.824 for EDTER; on Multicue the reported ODS is 0.921 versus 0.894; on PASCAL the reported ODS is 0.801 versus 0.752 for the next-best RCN. The authors present these results as evidence that training-free, quantum-inspired classical processing can reach state-of-the-art edge extraction while offering noise robustness and computational efficiency.

Load-bearing premise

The reported benchmark scores presuppose that the pointwise maximum of a binary Canny map and a raw Laplacian response is a valid, thresholdable edge-confidence map under the standard ODS/OIS/AP protocol; if that fusion is not such a map, the headline numbers would not be computable as published.

Editorial extensions

If this is right

  • If the reported scores hold, non-learned edge detection can surpass deep networks on four standard benchmarks, removing the need for labeled training data in edge-extraction applications.
  • The stated runtime and memory of 0.22 seconds and 65 MB on a single test image imply the method can run on embedded or low-power devices without specialized hardware.
  • The ablation tables imply the Schrödinger-inspired diffusion step is the main accuracy driver, adding about 0.085 ODS on the tested image compared with the same pipeline without it.
  • The noise-robustness experiments imply the method remains usable under Gaussian noise up to $\sigma=40$, which would be valuable wherever clean images are not guaranteed.

Reading between the lines

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

  • A fair stress test of the claimed advantage would recalibrate the Canny and Laplacian outputs into a common confidence scale before fusion; if the margin over EDTER shrinks under calibration, the reported gains may be an artifact of the raw maximum rule rather than of the diffusion preprocessing.
  • The update in Eq. (7) is mathematically the classical heat equation, not the time-dependent Schrödinger equation, so the 'quantum-inspired' framing is an analogy; the paper's practical claims would still stand, but the method is better characterized as a classical PDE-based detector.
  • The refined image $\psi(x,y,T)$ could be tested as a preprocessing front end for existing trained detectors; the ablation suggests the diffusion step may benefit downstream models without retraining.
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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 / 5 minor

Summary. The paper proposes a training-free edge detection pipeline composed of (i) Sobel gradient computation, (ii) iterative refinement by a Laplacian-based update starting from the grayscale image, and (iii) a hybrid edge map formed by taking the maximum of a Canny binary edge map and a Laplacian response. It reports state-of-the-art ODS/OIS/AP/F-measure numbers on NYUD, BSD500, Multicue, and PASCAL, and it includes ablations on BIPED images, noise robustness experiments, and runtime measurements. The central claim is that this classical, non-learned pipeline outperforms trained deep networks without any training.

Significance. If the reported results were valid, the paper would make a striking claim: a simple parameter-controlled classical pipeline surpassing deep edge detectors on standard benchmarks, with implications for low-resource edge detection. The claimed significance, however, depends entirely on (a) the iterative equation being correctly identified and analyzed, (b) the final edge map being a valid soft edge-confidence map for the standard threshold-sweep evaluation, and (c) the reported numbers being reproducible from specified parameters. As detailed below, none of these conditions is met in the manuscript. The paper is also notable for what it does not ship: no code, no parameter values for delta, T, or sigma, and no evaluation-protocol details.

major comments (4)
  1. [§III-B, Eq. (6)] Equation (6), ∂ψ/∂t = δ∇²ψ, is the linear heat (isotropic diffusion) equation, not the Schrödinger equation. The time-dependent Schrödinger equation is first-order in time and contains an imaginary unit, e.g., iħ∂ψ/∂t = -(ħ²/2m)∇²ψ + Vψ. The paper never introduces a potential term or complex phase, so the 'quantum-inspired' label is not supported by the mathematics. This is load-bearing because the claimed novelty of the refinement stage rests on the Schrödinger analogy.
  2. [§III-B, Eqs. (7)–(9)] The discrete implementation is not the PDE in Eq. (6). Eq. (7) computes ψ(t+Δt) = ψ(t) + δ∇²ψ(t), but Δt is never defined; the text simply uses δ as both diffusion coefficient and effective step size. Eq. (9) clips the result to [0,255] after every iteration, which makes the iterates a projected diffusion process rather than a solution of Eq. (6). No convergence, stability, or qualitative invariance under clipping is shown. Since this iteration is the entire preprocessing module, the manuscript must specify δ and T; it does not. Without these values the reported benchmark numbers cannot be reproduced.
  3. [§III-C, Eq. (22)] The final edge map defined by Eq. (22), E_hybrid = max(E_canny, E_lap), is not a soft edge-strength map. By Eq. (19), E_canny is binary {0,1}, while E_lap from Eqs. (20)–(21) is an unnormalized, signed second-derivative response. For any threshold τ < 1, every Canny edge pixel is classified as a positive detection regardless of E_lap; for τ > 1, only pixels with E_lap > τ are kept and all Canny edges are discarded. The precision-recall curve is therefore not a monotone ranking of boundary confidence, and the ODS/OIS/AP values in Tables I–IV are not well-defined from the published method. Algorithm 1's 'clip(∇²I_blurred,0,255)' does not fix this, because clipping does not make the binary component a graded response. An undocumented normalization or post-processing step would be needed to compute the reported scores.
  4. [§IV-B, Tables I–IV; §V] The quantitative claims are not reproducible from the manuscript. No evaluation details are reported: the paper does not state which test split is used for BSD500 or PASCAL, how boundaries are matched to ground truth (e.g., the standard BSDS tolerance protocol), or how ODS/OIS/AP are computed. The free parameters of the pipeline—δ, T, σ, and the Canny thresholds—are not specified except for two threshold values in the text; the ablations in Section V are computed on single images (RGB_025.png and butterfly.png), so dataset-level metrics such as ODS cannot be meaningfully reported for those tables. The reported state-of-the-art margins therefore cannot be verified independently.
minor comments (5)
  1. [§IV-B] Reference numbering is inconsistent: in the text, 'EDTER [67]' should be [55], 'BDCN [64]' should be [52], and several other in-text citations do not match the reference list.
  2. [Header] The paper contains an unfinished template header, '> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <', indicating a missing editorial step.
  3. [Fig. 3] Figure 3 is described in the text as comparing Roberts, Sobel, Prewitt, Canny, Edge-Net, and the proposed model, but the caption and surrounding discussion do not connect these to the quantitative noise tables; the qualitative results could be clearer.
  4. [Abstract and §IV] The abstract claims evaluation on BIPED, but BIPED is not included in the quantitative benchmark tables (Tables I–IV); it appears only in the single-image ablation.
  5. [§II] Section II's enumerated items are numbered as top-level list items (1–6), which produces confusing cross-references and is inconsistent with the rest of the paper's section numbering.

Circularity Check

1 steps flagged · score 2.0 of 10

No circularity in the empirical benchmark claim; the only definitional reduction is that the 'Schrödinger wave equation' refinement (Eq. 6) is the classical heat equation, so the quantum-inspired contribution is a renamed diffusion process.

  1. renaming known result [Section III-B, Eq. (6) (and Eq. (7))]
    "The Schrödinger analogy assumes that this intensity distribution can be treated as a time-evolving function ... ∂ψ(x,y,t)/∂t = δ · ∇²ψ(x,y,t) (6) ... ψ(x,y,t+Δt) = ψ(x,y,t) + δ · ∇²ψ(x,y,t) (7)"

    The paper names this step 'Quantum-Inspired Schrödinger Wave Evolution' and contribution 1 claims to 'integrate the Schrödinger wave equation into the edge detection process.' Equation (6) is exactly the linear heat/diffusion equation, not the Schrödinger equation: it has no imaginary unit, no potential term, and no ħ, and Eq. (7) is the standard explicit Euler discretization of isotropic diffusion. The 'quantum-inspired refinement' is therefore a relabeling of a classical diffusion operator; its stated edge-enhancing behavior is the known behavior of heat-equation smoothing, so the claimed novel mechanism reduces to a renamed classical result rather than a derivation from quantum mechanics.

full rationale

The paper's benchmark claims (ODS/OIS/AP/F-measure in Tables I-IV) are empirical outputs of a deterministic, training-free pipeline and are not defined in terms of the claimed results, so they are not circular in the fitted-input sense. There is no load-bearing self-citation: the one co-author citation ([21]) supports a general statement about fixed-parameter limitations and does not justify the method's output. The hybrid Canny+Laplacian fusion (Eq. 22) is a simple operator combination, and while the resulting map's compatibility with standard threshold-based evaluation is questionable, that is a correctness/reproducibility concern, not circularity. The one genuine definitional reduction is the 'Schrödinger wave equation' step: Eq. (6) is the classical heat equation renamed as quantum-inspired. That rename does not by itself force the reported benchmark numbers, so the overall circularity is minor and localized to the claimed novelty.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method rests on common image-processing assumptions (grayscale conversion, gradient filters) plus three ad hoc modeling choices: labeling heat diffusion as Schrödinger's, clipping during iteration, and fusing a binary map with a raw Laplacian response. No new physical entities are introduced.

free parameters (5)
  • diffusion coefficient delta = not reported
    Controls the Laplacian update rate in Eq (6)-(8); not stated for the main experiments.
  • time steps T = not reported
    Iteration count in Algorithm 1 and Eq (10); no value given.
  • Gaussian standard deviation sigma = not reported
    Standard deviation in Eq (12); only kernel size k=1 is stated.
  • Canny low threshold t_low = 50
    Set in Section III-C; no tuning procedure is described.
  • Canny high threshold t_high = 150
    Set in Section III-C; no tuning procedure is described.
assumptions (4)
  • domain assumption The intensity field psi(x,y,t) evolves according to the diffusion equation dpsi/dt = delta * Laplacian(psi), which the paper calls the Schrödinger wave equation.
    The equation (Eq 6) is the heat equation, not the Schrödinger equation; the physical analogy is unsupported.
  • ad hoc to paper Clipping psi to [0,255] after each update (Eq 9) does not change the qualitative behavior of the diffusion process.
    Clipping introduces a hard nonlinearity that is absent from the PDE in Eq (6); no stability or equivalence analysis is provided.
  • ad hoc to paper Taking the max of a binary Canny edge map and a raw Laplacian response (Eq 22) produces a valid edge-strength map for ODS/AP evaluation.
    The two maps have different scales and semantics, and the protocol for computing ODS/AP from this map is not described.
  • standard math RGB-to-grayscale conversion uses the fixed weights 0.2989, 0.5870, 0.1140 as in Eq (2).
    Standard luminance conversion; this assumption is not controversial.

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Cite this review

Pith. "Pith review of Training-free Quantum-Inspired Image Edge Extraction Method." pith.science (2026). https://pith.science/paper/65BZKRBS

@misc{pith2026250118929,
  author       = {Pith},
  title        = {Pith review of: Training-free Quantum-Inspired Image Edge Extraction Method},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/65BZKRBS}},
  note         = {Machine review of arXiv:2501.18929}
}
read the original abstract

Edge detection is a cornerstone of image processing, yet existing methods often face critical limitations. Traditional deep learning edge detection methods require extensive training datasets and fine-tuning, while classical techniques often fail in complex or noisy scenarios, limiting their real-world applicability. To address these limitations, we propose a training-free, quantum-inspired edge detection model. Our approach integrates classical Sobel edge detection, the Schr\"odinger wave equation refinement, and a hybrid framework combining Canny and Laplacian operators. By eliminating the need for training, the model is lightweight and adaptable to diverse applications. The Schr\"odinger wave equation refines gradient-based edge maps through iterative diffusion, significantly enhancing edge precision. The hybrid framework further strengthens the model by synergistically combining local and global features, ensuring robustness even under challenging conditions. Extensive evaluations on datasets like BIPED, Multicue, and NYUD demonstrate superior performance of the proposed model, achieving state-of-the-art metrics, including ODS, OIS, AP, and F-measure. Noise robustness experiments highlight its reliability, showcasing its practicality for real-world scenarios. Due to its versatile and adaptable nature, our model is well-suited for applications such as medical imaging, autonomous systems, and environmental monitoring, setting a new benchmark for edge detection.

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Reference graph

Works this paper leans on

74 extracted references · 68 canonical work pages

  1. [1]

    A 3x3 isotropic gradient operator for image processing,

    I. Sobel, G. Feldman, and others, “A 3x3 isotropic gradient operator for image processing,” a talk at the Stanford Artificial Project in , vol. 1968, pp. 271 –272, 1968

    work page 1968

  2. [2]

    Holistically-nested edge detection,

    S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1395–1403

    work page 2015

  3. [3]

    Sliding window based adaptative fuzzy measure for edge detection,

    C. Marco -Detchart, G. Lucca, M. A. Santos Silva, J. A. Rincon, V. Julian, and G. Dimuro, “Sliding window based adaptative fuzzy measure for edge detection,” Expert Syst, p. e13730

    work page

  4. [4]

    Quantum -inspired machine learning: a survey,

    L. Huynh, J. Hong, A. Mian, H. Suzuki, Y. Wu, and S. Camtepe, “Quantum -inspired machine learning: a survey,” arXiv preprint arXiv:2308.11269, 2023

    arXiv 2023

  5. [5]

    Quantum -enhanced hybrid feature engineering in thoracic CT image analysis for state -of-the-art nodule classification: an advanced lung cancer assessment,

    R. R. Shivwanshi and N. Nirala, “Quantum -enhanced hybrid feature engineering in thoracic CT image analysis for state -of-the-art nodule classification: an advanced lung cancer assessment,” Biomed Phys Eng Express, vol. 10, no. 4, p. 45005, 2024

    work page 2024

  6. [6]

    A hybrid quantum image edge detector for the NISQ era,

    A. Geng, A. Moghiseh , C. Redenbach, and K. Schladitz, “A hybrid quantum image edge detector for the NISQ era,” Quantum Mach Intell , vol. 4, no. 2, p. 15, 2022. 10 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE -CLICK HERE TO EDIT) <

    work page 2022

  7. [7]

    Contour detection and hierarchical image segmentation,

    P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Trans Pattern Anal Mach Intell , vol. 33, no. 5, pp. 898–916, 2010

    work page 2010

  8. [8]

    The Pascal Visual Object Classes Challenge: A Retrospective,

    M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes Challenge: A Retrospective,” Int J Comput Vis , vol. 111, no. 1, pp. 98 –136, Jan. 2015, doi: 10.1007/s11263-014-0733-5

    doi:10.1007/s11263-014-0733-5 2015

Show all 74 references
  1. [9]

    Learning to detect natural image boundaries using local brightness, color, and texture cues,

    D. R. Martin, C. C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” IEEE Trans Pattern Anal Mach Intell , vol. 26, no. 5, pp. 530 –549, 2004, doi: 10.1109/TPAMI.2004.1273918

    2004 arXiv

  2. [10]

    Contour detection based on nonclassical receptive field inhibition,

    C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Transactions on Image Processing, vol. 12, no. 7, pp. 729 –739, 2003, doi: 10.1109/TIP.2003.814250

    2003

  3. [11]

    Photo -Sketching: Inferring Contour Drawings From Images,

    M. Li, Z. L. Lin, R. Měch, E. Yumer, and D. Ramanan, “Photo -Sketching: Inferring Contour Drawings From Images,” 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1403 –1412, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:57375706

    2019

  4. [12]

    Dense extreme inception network: Towards a robust cnn model for edge detection,

    X. S. Poma, E. Riba, and A. Sappa, “Dense extreme inception network: Towards a robust cnn model for edge detection,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2020, pp. 1923–1932

    2020

  5. [13]

    A systematic comparison between visual cues for boundary detection,

    D. A. Mély, J. Kim, M. McGill, Y. Guo, and T. Serre, “A systematic comparison between visual cues for boundary detection,” Vision Res, vol. 120, pp. 93–107, Mar. 2016, doi: 10.1016/j.visres.2015.11.007

    2016 doi

  6. [14]

    Indoor segmentation and support inference from rgbd images,

    N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in Computer Vision –ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7 -13, 2012, Proceedings, Part V 12 , 2012, pp. 746–760

    2012

  7. [15]

    A computational approach to edge detection,

    J. Canny, “A computational approach to edge detection,” IEEE Trans Pattern Anal Mach Intell , no. 6, pp. 679–698, 1986

    1986

  8. [16]

    R. C. Gonzalez, Digital image processing . Pearson education india, 2009

    2009

  9. [17]

    Study and comparison of various image edge detection techniques,

    N. Chandrakar and D. Bhonsle, “Study and comparison of various image edge detection techniques,” International Journal of Managment, IT and Engineering, vol. 2, no. 5, pp. 499–509, 2012

    2012

  10. [18]

    The watershed transformation applied to image segmentation,

    S. Beucher, “The watershed transformation applied to image segmentation,” Scanning Microsc , vol. 1992, no. 6, p. 28, 1992

    1992

  11. [19]

    The design and use of steerable filters,

    W. T. Freeman, E. H. Adelson, and others, “The design and use of steerable filters,” IEEE Trans Pattern Anal Mach Intell , vol. 13, no. 9, pp. 891 –906, 1991

    1991

  12. [20]

    Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,

    S. Osher and J. A. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J Comput Phys , vol. 79, no. 1, pp. 12–49, 1988

    1988

  13. [21]

    Underwater Image Denoising Using Adaptive Retinal Processes and Smoothening Filters,

    P. K. Singh, B. Sharan, V. K. Kushwaha, S. P. Yadav, and S. Vats, “Underwater Image Denoising Using Adaptive Retinal Processes and Smoothening Filters,” in 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), 2023, pp. 329–334

    2023

  14. [22]

    Edge detection techniques using nonlinear diffusion-based models,

    T. Barbu, “Edge detection techniques using nonlinear diffusion-based models,” Bulletin of the Transilvania University of Brasov. Series III: Mathematics and Computer Science, pp. 199–210, 2023

    2023

  15. [23]

    Hierarchical Edge -Preserving Dense Matching by Exploiting Reliably Matched Line Segments,

    Y. Yue et al. , “Hierarchical Edge -Preserving Dense Matching by Exploiting Reliably Matched Line Segments,” Remote Sens (Basel) , vol. 15, no. 17, p. 4311, 2023

    2023

  16. [24]

    Quantum - Inspired Optimization in AI for Healthcare Networks,

    A. Arya, M. S. Devadas, T. V. H. Lakshmi, N. Yogeesh, R. R. Maaliw III, and others, “Quantum - Inspired Optimization in AI for Healthcare Networks,” in Quantum Networks and Their Applications in AI , IGI Global, 2024, pp. 197–213

    2024

  17. [25]

    N. B. Singh, From Schrödinger’s Equation to Deep Learning: A Quantum Approach. NB Singh, 2023

    2023

  18. [26]

    Quantum image processing and its application to edge detection: theory and experiment,

    X.-W. Yao et al., “Quantum image processing and its application to edge detection: theory and experiment,” Phys Rev X, vol. 7, no. 3, p. 31041, 2017

    2017

  19. [27]

    Physics-informed computer vision: A review and perspectives,

    C. Banerjee, K. Nguyen, C. Fookes, and K. George, “Physics-informed computer vision: A review and perspectives,” ACM Comput Surv , vol. 57, no. 1, pp. 1–38, 2024

    2024

  20. [28]

    WAVE - BASED SIGNAL SECURITY AND PRIVACY STUDIES USING AUTOMATIC HIGH -BIT-RATE OPTICAL COMMUNICATIONS WITH QUANTUM CRYPTOGRAPHIC,

    U. Allimuthu, S. Balasundaram, and others, “WAVE - BASED SIGNAL SECURITY AND PRIVACY STUDIES USING AUTOMATIC HIGH -BIT-RATE OPTICAL COMMUNICATIONS WITH QUANTUM CRYPTOGRAPHIC,” 2023

    2023

  21. [29]

    Attention-driven active sensing with hybrid neural network for environmental field mapping,

    T. Li, C. Wang, M. Q. -H. Meng, and C. W. de Silva, “Attention-driven active sensing with hybrid neural network for environmental field mapping,” IEEE Transactions on Automation Science and Engineering , vol. 19, no. 3, pp. 2135–2152, 2021

    2021

  22. [30]

    Multi -scale spatial context -based semantic edge detection,

    W. Ma, C. Gong, S. Xu, and X. Zhang, “Multi -scale spatial context -based semantic edge detection,” Information Fusion, vol. 64, pp. 238–251, 2020

    2020

  23. [31]

    Edge preserving and multi -scale contextual neural network for salient object detection,

    X. Wang, H. Ma, X. Chen, and S. You, “Edge preserving and multi -scale contextual neural network for salient object detection,” IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 121–134, 2017

    2017

  24. [32]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778

    2016

  25. [33]

    A Survey of Image Translation Based on Conditional Generative Adversarial Networks,

    T. Hangyao, W. Wanliang, C. Jiacheng, L. Guoqing , and W. Fei, “A Survey of Image Translation Based on Conditional Generative Adversarial Networks,” Journal of Computer -Aided Design & Computer Graphics, 2024

    2024

  26. [34]

    X. Zeng, M. Xu, Y. Hu, H. Tang, Y. Hu, and L. Nie, “Adaptive edge -aware semantic interaction network for salient object detection in optical remote sensing 11 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE -CLICK HERE TO EDIT) < images,” IEEE Transactions on Geosc...

    2023

  27. [35]

    Eatder: Edge -assisted adaptive transformer detector for remote sensing change detection,

    J. Ma, J. Duan, X. Tang, X. Zhang, and L. Jiao, “Eatder: Edge -assisted adaptive transformer detector for remote sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023

    2023

  28. [36]

    GAN -FM: Infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators,

    H. Zhang, J. Yuan, X. Tian, and J. Ma, “GAN -FM: Infrared and visible image fusion using GAN with full-scale skip connection and dual Markovian discriminators,” IEEE Trans Comput Imaging , vol. 7, pp. 1134–1147, 2021

    2021

  29. [37]

    Quantum -inspired computational imaging,

    Y. Altmann, S. McLaughlin, M. J. Padgett, V. K. Goyal, A. O. Hero, and D. Faccio, “Quantum -inspired computational imaging,” Science (1979), vol. 361, no. 6403, p. eaat2298, 2018

    1979

  30. [38]

    A nobel hybrid approach for edge detection,

    P. Rani and P. Tanwar, “A nobel hybrid approach for edge detection,” International Journal of Computer Science and Engineering Survey , vol. 4, no. 2, p. 27, 2013

    2013

  31. [39]

    Injected infrared and visible image fusion via l_ {1} decomposition model and guided filtering,

    H. Yan, J.-X. Zhang, and X. Zhang, “Injected infrared and visible image fusion via l_ {1} decomposition model and guided filtering,” IEEE Trans Comput Imaging, vol. 8, pp. 162–173, 2022

    2022

  32. [40]

    SMFuse: Multi - focus image fusion via self -supervised mask - optimization,

    J. Ma, Z. Le, X. Tian, and J. Jiang, “SMFuse: Multi - focus image fusion via self -supervised mask - optimization,” IEEE Trans Comput Imaging , vol. 7, pp. 309–320, 2021

    2021

  33. [41]

    Quantum inspired approach for denoising with application to medical imaging,

    A. Hashemi, S. Dutta, B. Georgeot, D. Kouamé, and H. Sabet, “Quantum inspired approach for denoising with application to medical imaging,” ArXiv, p. arXiv– 2405, 2024

    2024

  34. [42]

    An improved quantum -inspired differential evolution algorithm for deep belief network,

    W. Deng, H. Liu, J. Xu, H. Zhao, and Y. Song, “An improved quantum -inspired differential evolution algorithm for deep belief network,” IEEE Trans Instrum Meas, vol. 69, no. 10, pp. 7319–7327, 2020

    2020

  35. [43]

    Quantum edge detection for image segmentation in optical environments,

    M. Mastriani, “Quantum edge detection for image segmentation in optical environments,” arXiv preprint arXiv:1409.2918, 2014

    2014 arXiv

  36. [44]

    Quantum Wave Packet Transforms with compact frequency support,

    H. Ni and L. Ying, “Quantum Wave Packet Transforms with compact frequency support,” arXiv preprint arXiv:2405.00929, 2024

    2024 arXiv

  37. [45]

    A quadratic time -dependent quantum harmonic oscillator,

    F. E. Onah, E. Garc \’\ia Herrera, J. A. Ruelas -Galván, G. Juárez Rangel, E. Real Norzagaray, and B. M. Rodr\’\iguez-Lara, “A quadratic time -dependent quantum harmonic oscillator,” Sci Rep, vol. 13, no. 1, p. 8312, 2023

    2023

  38. [46]

    Quantum Image Processing: Leveraging Quantum Kernels for Enhanced Computational Efficiency and Capability,

    D. C. Youvan, “Quantum Image Processing: Leveraging Quantum Kernels for Enhanced Computational Efficiency and Capability,” 2024

    2024

  39. [47]

    Fast edge detection using structured forests,

    P. Dollár and C. L. Zitnick, “Fast edge detection using structured forests,” IEEE Trans Pattern Anal Mach Intell, vol. 37, no. 8, pp. 1558–1570, 2014

    2014

  40. [48]

    Semicontour: A semi -supervised learning approach for contour detection,

    Z. Zhang, F. Xing, X. Shi, and L. Yang, “Semicontour: A semi -supervised learning approach for contour detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 251–259

    2016

  41. [49]

    Oriented edge forests for boundary detection,

    S. Hallman and C. C. Fowlkes, “Oriented edge forests for boundary detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1732–1740

    2015

  42. [50]

    Richer convolutional features for edge detection,

    Y. Liu, M. -M. Cheng, X. Hu, K. Wang, and X. Bai, “Richer convolutional features for edge detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3000–3009

    2017

  43. [51]

    Learning to predict crisp boundaries,

    R. Deng, C. Shen, S. Liu, H. Wang, and X. Liu, “Learning to predict crisp boundaries,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 562–578

    2018

  44. [52]

    Bi-directional cascade network for perceptual edge detection,

    J. He, S. Zhang, M. Yang, Y. Shan, and T. Huang, “Bi-directional cascade network for perceptual edge detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 3828–3837

    2019

  45. [53]

    Learning deep structured multi - scale features using attention -gated crfs for contour prediction,

    D. Xu, W. Ouyang, X. Alameda -Pineda, E. Ricci, X. Wang, and N. Sebe, “Learning deep structured multi - scale features using attention -gated crfs for contour prediction,” Adv Neural Inf Process Syst , vol. 30, 2017

    2017

  46. [54]

    Pixel difference networks for efficient edge detection,

    Z. Su et al. , “Pixel difference networks for efficient edge detection,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 5117–5127

    2021

  47. [55]

    Edter: Edge detection with transformer,

    M. Pu, Y. Huang, Y. Liu, Q. Guan, and H. Ling, “Edter: Edge detection with transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 1402–1412

    2022

  48. [56]

    Sledge: Sequential labeling of image edges for boundary detection,

    N. Payet and S. Todorovic, “Sledge: Sequential labeling of image edges for boundary detection,” Int J Comput Vis, vol. 104, pp. 15–37, 2013

    2013

  49. [57]

    Perceptual organization and recognition of indoor scenes from RGB-D images,

    S. Gupta, P. Arbelaez, and J. Malik, “Perceptual organization and recognition of indoor scenes from RGB-D images,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 564–571

    2013

  50. [58]

    Sketch tokens: A learned mid -level representation for contour and object detection,

    J. J. Lim, C. L. Zitnick, and P. Dollár, “Sketch tokens: A learned mid -level representation for contour and object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3158–3165

    2013

  51. [59]

    Multiscale combinatorial grouping for image segmentation and object proposal generation,

    J. Pont -Tuset, P. Arbelaez, J. T. Barron, F. Marques, and J. Malik, “Multiscale combinatorial grouping for image segmentation and object proposal generation,” IEEE Trans Pattern Anal Mach Intell , vol. 39, no. 1, pp. 128–140, 2016

    2016

  52. [60]

    Smoothing proximal gradient method for general structured sparse learning,

    X. Chen, Q. Lin, S. Kim, J. G. Carbonell, and E. P. Xing, “Smoothing proximal gradient method for general structured sparse learning,” arXiv preprint arXiv:1202.3708, 2012

    2012 arXiv

  53. [61]

    Visual boundary prediction: A deep neural prediction network and quality dissection,

    J. Kivinen, C. Williams, and N. Heess, “Visual boundary prediction: A deep neural prediction network and quality dissection,” in Artificial intelligence and statistics, 2014, pp. 512–521

    2014

  54. [62]

    Crisp boundary detection using pointwise mutual information,

    P. Isola, D. Zoran, D. Krishnan, and E. H. Adelson, “Crisp boundary detection using pointwise mutual information,” in Computer Vision –ECCV 2014: 13th 12 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE -CLICK HERE TO EDIT) < European Conference, Zurich, Switzerland,...

    2014

  55. [63]

    Pixel -wise deep learning for contour detection,

    J.-J. Hwang and T. -L. Liu, “Pixel -wise deep learning for contour detection,” arXiv preprint arXiv:1504.01989, 2015

    2015 arXiv

  56. [64]

    Segmenting natural images with the least effort as humans.,

    Q. Zhao, “Segmenting natural images with the least effort as humans.,” in BMVC, 2015, pp. 110–111

    2015

  57. [65]

    Deepcontour: A deep convolutional feature learned by positive -sharing loss for contour detection,

    W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang, “Deepcontour: A deep convolutional feature learned by positive -sharing loss for contour detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3982–3991

    2015

  58. [66]

    Object contour detection with a fully convolutional encoder-decoder network,

    J. Yang, B. Price, S. Cohen, H. Lee, and M. -H. Yang, “Object contour detection with a fully convolutional encoder-decoder network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 193–202

    2016

  59. [67]

    -fields: Neural network nearest neighbor fields for image transforms,

    Y. Ganin and V. Lempitsky, “ -fields: Neural network nearest neighbor fields for image transforms,” in Asian conference on computer vision, 2014, pp. 536–551

    2014

  60. [68]

    Fined: Fast inference network for edge detection,

    J. K. Wibisono and H. -M. Hang, “Fined: Fast inference network for edge detection,” in 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021, pp. 1–6

    2021

  61. [69]

    Learning relaxed deep supervision for better edge detection,

    Y. Liu and M. S. Lew, “Learning relaxed deep supervision for better edge detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 231–240

    2016

  62. [70]

    Convolutional oriented boundaries,

    K.-K. Maninis, J. Pont -Tuset, P. Arbeláez, and L. Van Gool, “Convolutional oriented boundaries,” in Computer Vision –ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 , 2016, pp. 580 – 596

    2016

  63. [71]

    Rindnet: Edge detection for discontinuity in reflectance, illumination, normal and depth,

    M. Pu, Y. Huang, Q. Guan, and H. Ling, “Rindnet: Edge detection for discontinuity in reflectance, illumination, normal and depth,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 6879–6888

    2021

  64. [72]

    Deep structural contour detection,

    R. Deng and S. Liu, “Deep structural contour detection,” in Proceedings of the 28th ACM international conference on multimedia , 2020, pp. 304–312

    2020

  65. [73]

    Object contour and edge detection with refinecontournet,

    A. P. Kelm, V. S. Rao, and U. Zölzer, “Object contour and edge detection with refinecontournet,” in Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3 –5, 2019, Proceedings, Part I 18 , 2019, pp. 246–258

    2019

  66. [74]

    Multilevel edge features guided network for image denoising,

    F. Fang, J. Li, Y. Yuan, T. Zeng, and G. Zhang, “Multilevel edge features guided network for image denoising,” IEEE Trans Neural Netw Learn Syst , vol. 32, no. 9, pp. 3956–3970, 2020. Arti Jain received her Bachelor of Science (BSc) in Computer Science and Mathematics in 2018 ...

    2020

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.