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arxiv: 2604.16609 · v1 · submitted 2026-04-17 · 💻 cs.CV

Recognition: unknown

IncepDeHazeGAN: Novel Satellite Image Dehazing

Tejeswar Pokuri , Shivarth Rai
Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:01 UTC · model grok-4.3

classification 💻 cs.CV
keywords dehazingsatellite imagerygenerative adversarial networksInception blocksfeature fusionimage restorationremote sensingGrad-CAM
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The pith

IncepDeHazeGAN combines Inception blocks with multi-layer feature fusion in a GAN to dehaze satellite images.

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

This paper introduces IncepDeHazeGAN as a new generative adversarial network for single-image dehazing of satellite data affected by clouds or fog. The design uses Inception blocks to pull out features at multiple scales while a multi-layer fusion step reuses information from different convolution stages several times. Grad-CAM visualizations are included to show how the network shifts focus across varying haze levels. The authors report that experiments on several datasets place the model at state-of-the-art performance for restoring clear remote-sensing images.

Core claim

IncepDeHazeGAN is a generative adversarial network that integrates Inception blocks for multi-scale feature extraction and a multi-layer feature fusion design that merges outputs from successive convolution layers multiple times. This structure is presented as a way to recover high-quality clear images from hazy satellite captures in remote sensing. The addition of Grad-CAM explanations is used to illustrate the regions the network attends to under different haze conditions.

What carries the argument

IncepDeHazeGAN's core mechanism is the pairing of Inception blocks, which extract multi-scale features, with repeated multi-layer feature fusion that reuses convolutional outputs across layers for efficient information flow.

If this is right

  • Hazy satellite images can be restored to higher visual quality through multi-scale feature extraction.
  • Repeated fusion of features from different layers enables more efficient use of extracted information.
  • The network adapts its focus to different haze densities as revealed by Grad-CAM maps.
  • State-of-the-art results on multiple datasets support broader use of the model for remote sensing restoration tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same block combination could be tested on other image-degradation problems such as underwater or aerial photography.
  • Grad-CAM outputs might serve as a diagnostic tool to refine fusion strategies in future dehazing networks.
  • Success on satellite data suggests the design could generalize to real-time processing of ground-based hazy scenes without major changes.

Load-bearing premise

The assumption that adding Inception blocks and repeated multi-layer feature fusion to a GAN will produce better dehazing results on satellite images than prior methods.

What would settle it

A side-by-side test on standard satellite dehazing benchmarks where IncepDeHazeGAN fails to exceed existing methods on quantitative measures such as PSNR or SSIM.

Figures

Figures reproduced from arXiv: 2604.16609 by Shivarth Rai, Tejeswar Pokuri.

Figure 1
Figure 1. Figure 1: Sample Pair from Haze1k dataset (a) Hazy Image (b) Clear Image [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Sample Pair from RICE dataset 4 The Methodology: IncepDeHazeGAN Our proposed Model follows a GAN (Generative Adversial Networks) based ap￾proach to image dehazing. The network diagram is [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Model overview (1x1, 1x3, 3x1, 3x3) to extract features. We have used the residual connections, as used in [14]. The major purpose behind employing skip connections in the architecture was to share the low-level features learned at initial convolution layers to the deconvolution layers and reduce information loss due to downsam￾pling operations. This concatenation of feature maps helped them to generate th… view at source ↗
Figure 4
Figure 4. Figure 4: IncepDehazeGan Generator \mathcal {L}_{\text {L1}} = \frac {1}{N} \sum _{i=1}^{N} | y_i - \hat {y}_i | (4) The total generator loss is a weighted sum of the adversarial loss and the L1 loss, where \lambda is a hyperparameter controlling the trade-off between these two losses. In our implementation, \lambda is set to 100: \mathcal {L}_{\text {gen}} = \mathcal {L}_{\text {GAN}} + \lambda \cdot \mathcal {L}_{… view at source ↗
Figure 5
Figure 5. Figure 5: IncepDehazeGan Discriminator which encourages the generator to produce realistic local features and textures across the image. By focusing on small regions for each pixel, the discriminator also helps mitigate overfitting. For training the Discriminator, we use Binary Cross Entropy to distinguish between Ground Truth and Output produced by generator. BCE loss has 2 components - real loss and generated loss… view at source ↗
Figure 6
Figure 6. Figure 6: An example visualization from GradCAM highlights that the model predomi￾nantly concentrates on the hazy regions [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparisons of dehazed results by different methods on Haze1k dataset. (g–i) are samples from the thin, moderate, and thick haze subsets of the Haze1k test set, respectively Input CEP HL EVPM IDeRs AOD LDN RSH AUNet LRSDNOurs GT [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual comparisons of dehazed results by different methods on the RICE dataset. RICE For training, this dataset was split into a 90:10 ratio with 90% used for training and 10% used for testing. Our network achieves an SSIM of 0.9513, PSNR of 28.612 and FSIM of 0.9616. 5.3 Comparsion with other Models Our model delivers remarkable results on both datasets. Starting with Haze1k, where many novel models strug… view at source ↗
read the original abstract

Dehazing is a technique in computer vision for enhancing the visual quality of images captured in cloudy or foggy conditions. Dehazing helps to recover clear, high-quality images from haze-affected remote sensing data. In this study, we introduce IncepDeHazeGAN, a novel Generative Adversarial Network (GAN) involving Inception block and multi-layer feature fusion for the task of single-image dehazing. Utilizing the Inception block allows for multi-scale feature extraction. On the other hand, the multi-layer feature fusion design achieves efficient reuse of features as the features extracted at different convolution layers are fused several times. Grad-CAM XAI technique has been applied to our network, highlighting the regions focused on by the network for dehazing and its adaptation to different haze conditions. Experiments demonstrate that our network achieves state-of-the-art results in several datasets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces IncepDeHazeGAN, a GAN architecture combining Inception blocks for multi-scale feature extraction with multi-layer feature fusion for efficient reuse, targeted at single-image dehazing of satellite imagery. It applies Grad-CAM for visualizing network attention under varying haze conditions and asserts that experiments establish state-of-the-art performance across several datasets.

Significance. If the superiority claim holds under proper evaluation, the architecture could advance remote-sensing dehazing by integrating multi-scale processing and repeated feature fusion within a GAN, while the Grad-CAM analysis would add interpretability value for understanding adaptation to haze levels.

major comments (2)
  1. [Abstract] Abstract: the assertion that 'Experiments demonstrate that our network achieves state-of-the-art results in several datasets' is unsupported by any PSNR/SSIM values, comparison tables, named datasets or splits, training protocol, or ablation results, so the central empirical claim cannot be assessed.
  2. [Experiments] Experiments section (or equivalent): no quantitative results, baseline comparisons, or statistical validation are supplied to demonstrate that the Inception + multi-layer fusion design measurably outperforms prior single-image dehazing methods on representative satellite data.
minor comments (1)
  1. [Abstract] Abstract: 'involving Inception block' should read 'Inception blocks' for grammatical consistency with the plural usage later in the sentence.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We agree that the current manuscript version does not supply the quantitative evidence needed to substantiate the state-of-the-art claims, and we will perform a major revision to address this.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that 'Experiments demonstrate that our network achieves state-of-the-art results in several datasets' is unsupported by any PSNR/SSIM values, comparison tables, named datasets or splits, training protocol, or ablation results, so the central empirical claim cannot be assessed.

    Authors: We acknowledge that the abstract's claim is unsupported by any numerical evidence or references within the current text. In the revised manuscript we will shorten the claim and add a concise statement of the key datasets, metrics (PSNR/SSIM), and performance margins, while explicitly directing readers to the expanded Experiments section for tables, splits, training details, and ablations. revision: yes

  2. Referee: [Experiments] Experiments section (or equivalent): no quantitative results, baseline comparisons, or statistical validation are supplied to demonstrate that the Inception + multi-layer fusion design measurably outperforms prior single-image dehazing methods on representative satellite data.

    Authors: The referee correctly identifies the absence of quantitative results. We will add a full Experiments section containing: (i) PSNR and SSIM tables on named satellite dehazing datasets with explicit train/val/test splits, (ii) direct comparisons against representative prior single-image dehazing methods, (iii) ablation studies isolating the Inception blocks and multi-layer fusion components, and (iv) basic statistical validation (means and standard deviations over repeated runs). revision: yes

Circularity Check

0 steps flagged

No circularity; architecture is an explicit ansatz and SOTA claim is empirical

full rationale

The paper presents IncepDeHazeGAN as a novel GAN design using Inception blocks for multi-scale features and multi-layer fusion for feature reuse. These choices are motivated by standard computer-vision practices rather than derived from prior self-citations or fitted parameters. No equations define a quantity in terms of itself, no 'prediction' reduces to a training fit by construction, and no uniqueness theorem or ansatz is smuggled via self-citation. The SOTA claim rests on (undetailed) experiments; while this leaves the claim unsupported, it does not create circularity because the result is not presupposed by the network definition. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities beyond the standard assumptions of deep-learning image processing; the central claim rests on the unverified superiority of the proposed architecture.

axioms (1)
  • domain assumption Standard deep-learning assumptions for image-to-image translation tasks hold (e.g., adversarial training converges to useful solutions).
    Implicit in any GAN-based dehazing claim.
invented entities (1)
  • IncepDeHazeGAN no independent evidence
    purpose: A GAN architecture for single-image satellite dehazing that incorporates Inception blocks and multi-layer feature fusion.
    The paper introduces this named model as its primary contribution.

pith-pipeline@v0.9.0 · 5439 in / 1203 out tokens · 36098 ms · 2026-05-10T08:01:24.036978+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

22 extracted references · 19 canonical work pages · 1 internal anchor

  1. [1]

    Morphological and cytoskeleton changes in cells after EMT

    Lee, G. Y., Chen, J., Dam, T., Ferdaus, M. M., Poenar, D. P., & Duong, V. N. (2024). Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2405.07520 2.Zhang,S.,Zhao,L.,Hu,K.,Feng,S.,En,F.,&Zhao,L.(092023).Deepguided transformer dehazing network. Scientifi...

    work page doi:10.1038/s41598-023- 2024

  2. [2]

    G., & Nayar, S

    Narasimhan, S. G., & Nayar, S. K. (2002). Vision and the Atmosphere. Inter- national Journal of Computer Vision, 48(3), 233–254. doi:10.1023/A:1016328200723

    work page doi:10.1023/a:1016328200723 2002

  3. [3]

    (12 2015)

    Narasimhan, S., & Nayar, S. (12 2015). Interactive (De)weathering of an im- age using physical models. IEEE Workshop on Color and Photometric Methods in Computer Vision, 10

    2015

  4. [4]

    (11 2018)

    Berman, D., Treibitz, T., & Avidan, S. (11 2018). Single Image Dehazing Using Haze-Lines. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP, 1–1. doi:10.1109/TPAMI.2018.2882478

    work page doi:10.1109/tpami.2018.2882478 2018

  5. [5]

    (12 2017)

    Jiang, Y., Sun, C., Zhao, Y., & Yang, L. (12 2017). Image Dehazing Using Adaptive Bi-Channel Priors on Superpixels. Computer Vision and Image Under- standing, 165, 17–32. doi:10.1016/j.cviu.2017.10.014

    work page doi:10.1016/j.cviu.2017.10.014 2017

  6. [6]

    M., & Kim, W

    Bui, T. M., & Kim, W. (2018). Single Image Dehazing Using Color Ellipsoid Prior. IEEE Transactions on Image Processing, 27(2), 999–1009. doi:10.1109/ TIP.2017.2771158

    work page arXiv 2018

  7. [7]

    Generative Adversarial Networks

    Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,S.,... Bengio,Y.(2014).GenerativeAdversarialNetworks.arXivStat.ML. Retrieved fromhttp://arxiv.org/abs/1406.2661

    work page internal anchor Pith review arXiv 2014

  8. [8]

    Going Deeper with Convolutions

    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... Ra- binovich, A. (2014). Going Deeper with Convolutions. arXiv Cs.CV. Retrieved fromhttp://arxiv.org/abs/1409.4842

    work page Pith review arXiv 2014

  9. [9]

    He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Im- age Recognition. arXiv Cs.CV. Retrieved fromhttp://arxiv.org/abs/1512. 03385

    2015

  10. [10]

    Xu, G., Wang, X., Wu, X., Leng, X., & Xu, Y. (2024). Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey. arXiv Eess.IV. Retrieved fromhttp://arxiv.org/abs/ 2405.01725

    work page arXiv 2024

  11. [11]

    Dong, S., & Chen, Z. (2021). A Multi-Level Feature Fusion Network for Re- mote Sensing Image Segmentation. Sensors, 21(4). doi:10.3390/s21041267

    work page doi:10.3390/s21041267 2021

  12. [12]

    He, Y., Li, C., Li, X., & Bai, T. (2024). A Lightweight CNN Based on Axial Depthwise Convolution and Hybrid Attention for Remote Sensing Image Dehaz- ing. Remote Sensing, 16(15). doi:10.3390/rs16152822

    work page doi:10.3390/rs16152822 2024

  13. [13]

    Du, Y., Li, J., Sheng, Q., Zhu, Y., Wang, B., & Ling, X. (2024). Dehazing Network: Asymmetric Unet Based on Physical Model. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–12. doi:10.1109/TGRS.2024.3359217

    work page doi:10.1109/tgrs.2024.3359217 2024

  14. [14]

    and Carlsson, Marcel , month = apr, year =

    Wen, Y., Gao, T., Li, Z., Zhang, J., & Chen, T. (04 2024). Encoder-Minimal IncepDeHazeGAN: Novel Satellite Image Dehazing 13 and Decoder-Minimal Framework for Remote Sensing Image Dehazing. 36–40. doi:10.1109/ICASSP48485.2024.10446125

    work page doi:10.1109/icassp48485.2024.10446125 2024

  15. [15]

    Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D. (2017). AOD-Net: All-in-One Dehazing Network. 2017 IEEE International Conference on Computer Vision (ICCV), 4780–4788. doi:10.1109/ICCV.2017.511 17.Li,Y.,&Chen,X.(2021).ACoarse-to-FineTwo-StageAttentiveNetworkfor Haze Removal of Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 18(10), 1751–1...

    work page doi:10.1109/iccv.2017.511 2017

  16. [16]

    Han, J., Zhang, S., Fan, N., & Ye, Z. (2022). Local patchwise minimal and maximal values prior for single optical remote sensing image dehazing. Informa- tion Sciences, 606, 173–193. doi:10.1016/j.ins.2022.05.033

    work page doi:10.1016/j.ins.2022.05.033 2022

  17. [17]

    (03 2019)

    Xu, L., Tree, P., Yan, Y., Kwong, S., Chen, J., & Duan, L.-Y. (03 2019). IDeRS: Iterative Dehazing Method for Single Remote Sensing Image. Informa- tion Sciences, 489. doi:10.1016/j.ins.2019.02.058

    work page doi:10.1016/j.ins.2019.02.058 2019

  18. [18]

    Ullah, H., Muhammad, K., Irfan, M., Anwar, S., Sajjad, M., Imran, A., & Albuquerque, V. H. C. (10 2021). Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing. IEEE Transactions on Image Process- ing, PP, 1–1. doi:10.1109/TIP.2021.3116790

    work page doi:10.1109/tip.2021.3116790 2021

  19. [19]

    R.et al.Grad-cam: Visual explanations from deep networks via gradient-based localization.International Journal of Computer Vision128, 336–359 (2019)

    Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2019). Grad-CAM: Visual Explanations from Deep Networks via Gradient- Based Localization. International Journal of Computer Vision, 128(2), 336–359. doi:10.1007/s11263-019-01228-7

    work page doi:10.1007/s11263-019-01228-7 2019

  20. [20]

    (03 2020)

    Huang, B., Li, Z., Yang, C., Sun, F., & Song, Y. (03 2020). Single Satellite Optical Imagery Dehazing using SAR Image Prior Based on Conditional Gener- ative Adversarial Networks. 1795–1802. doi:10.1109/WACV45572.2020.9093471

    work page doi:10.1109/wacv45572.2020.9093471 2020

  21. [21]

    Lin, D., Xu, G., Wang, X., Wang, Y., Sun, X., & Fu, K. (2019). A Re- mote Sensing Image Dataset for Cloud Removal. arXiv Cs.CV. Retrieved from http://arxiv.org/abs/1901.00600

    work page arXiv 2019

  22. [22]

    Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2018). Image-to-Image Trans- lation with Conditional Adversarial Networks

    2018