Title: MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Degradation Estimation

Adverse Weather

Image Restoration
Abstract: Restoring images degraded by adverse weather remains a significant challenge due to the highly non-uniform and spatially heterogeneous nature of weather-induced artifacts, \emph{e.g.}, fine-grained rain streaks versus widespread haze. Accurately estimating the underlying degradation can intuitively provide restoration models with more targeted and effective guidance, enabling adaptive processing strategies. To this end, we propose a Morton-Order Degradation Estimation Mechanism (MODEM) for adverse weather image restoration. Central to MODEM is the Morton-Order 2D-Selective-Scan Module (MOS2D), which integrates Morton-coded spatial ordering with selective state-space models to capture long-range dependencies while preserving local structural coherence. Complementing MOS2D, we introduce a Dual Degradation Estimation Module (DDEM) that disentangles and estimates both global and local degradation priors. These priors dynamically condition the MOS2D modules, facilitating adaptive and context-aware restoration. Extensive experiments and ablation studies demonstrate that MODEM achieves state-of-the-art results across multiple benchmarks and weather types, highlighting its effectiveness in modeling complex degradation dynamics. Our code will be released soon.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a Morton-Order Degradation Estimation Mechanism for adverse weather image restoration. A Morton-Order 2D-Selective-Scan Module is developed to capture long-range dependencies while preserving local structural coherence. Meanwhile, a Dual Degradation Estimation Module is proposed to disentangles degradations. The idea of employing SSM is recently popular in this field, and the newly designed model sounds interesting.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1) Using Morton Scanning instead of standard Scanning ways brings new insight to this community.\n\n2) The proposed MODEM performs favorably against the considered methods.\n\nCons:\n\n1) Differences between MODEM and existing SSM-based methods (e.g., MambaIR[1], FreqMamba[2]) are supposed to be further analyzed.\n\n2) The space/time complexity of the proposed algorithm is not studied.\n\n[1] Mambair: A simple baseline for image restoration with state-space model. ECCV24.\n\n[2] Freqmamba: Viewing mamba from a frequency perspective for image deraining. ACM MM24.\n\nQUESTIONS:\n1) The authors are encouraged to include comparisons with more recent models in Tables 2–4, especially works from 2023–2024 such as ConvIR[3], AST[4], CODE[5], and FSNet[6], which are highly relevant in this context.\n\n2) In Tab.1, a comparison with the diffusion-based method in terms of PSNR/SSIM is not enough, more perceptual metric is supposed to be provided, such as LPIPS.\n\n3) Complexity Analysis is conducted, while no SSM-based methods are included, such as MambaIR[1], FreqMamba[2].\n\n4) Effective Receptive Field (ERF) visualization of MODEM and competitors (e.g., MambaIR[1]) would help in understanding their long-range modeling ability.\n\n5) Quantitative results (such as non-reference metric, NIQE) or a user study on challenging real-world scenes should be conducted.\n\n6) The ablation study could be extended to explore the contribution of different loss components\n\n[3] Revitalizing Convolutional Network for Image Restoration. TPAMI24.\n\n[4] Adapt or perish: Adaptive sparse transformer with attentive feature refinement for image restoration. CVPR24.\n\n[5] Comprehensive and delicate: An efficient transformer for image restoration. CVPR23.\n\n[6] Image Restoration via Frequency Selection. TPAMI24.\n\nLIMITATIONS:\nLimitation of the method is not discussed.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the Morton-Order Degradation Estimation Mechanism (MODEM) for adverse weather removal. MODEM integrates a Dual Degradation Estimation Module (DDEM) for extracting global and local degradation priors, with a Morton-Order 2D-Selective-Scan Module (MOS2D) that employs Morton-coded spatial ordering and selective state-space models for adaptive, context-aware restoration. Extensive experiments demonstrate MODEM’s state-of-the-art performance across multiple benchmarks and weather types.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths.\n\n1, This paper is well-written and easy to follow. Moreover, this paper has clear structure and is easy to understand.\n\n2, Morton-Order Degradation Estimation Mechanism seems to be interesting, which integrates Morton-coded spatial ordering with selective state space modeling to effectively capture spatially heterogeneous weather degradation dynamics.\n\nWeaknesses.\nI find that this paper exists some major concerns.\n\n1, Figure 2 and Figure 2 may be inappropriate. First of all, in Figure 2, the clear image is not equal to the sum of these three component. Putting it in the paper may confuse readers. Moreover, I do not find the significant meaning for putting the Figure 3 in current position. It may be meaningful but not major. The authors should consider to replace these two figures with more convincing demonstration.\n\n2, I also find that in Tables 2-4,  the authors compare with latest method which is published in 2022. The authors should compare with recent state-of-the-art approaches. Comparing with outdate methods is not acceptable.\n\n3, I observe that, in Table 5, the full model is not better than the model without DSAM in the datasets of Snow100K-S and Snow100K-L. The authors should give an in-depth analysis and explanations.\n\n4, The authors do not give the analysis about the two-stage training mechanism. How about only training the first stage without using GT within the same training epoch? Where does the correlation loss derive from? Why not the authors analyze this? Why the authors computing the KL loss at the position of $\\tilde{Z}$? Why not compute the vectors at the position of the feature of Figure 4(b), i.e., the feature before the operation of avg pool.\n\n5, The authors also should compare the results of different scanning manners including, (a) Raster Scanning (b) Continuous Scanning (c) Local Scanning, which is illustrated in Figure 1.\n\nQUESTIONS:\nSee the Weaknesses\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose MODEM, SSM-based image restoration model for adverse weather removal.\nBeside general scanning methods, they utilize Morton scanning to better preserve spatial locality and capture contextual information.\nTo maximize the effect of Morton scanning, they introduce MOS2D module and training strategies.\nWith those contributions, their model achieves a significant margin over previous models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Although the number of parameters slightly increases, it achieves faster inference speed and better performance compared to the existing state-of-the-art.\n2. MODEM achieves substantial performance improvement on the Allweather dataset.\n3. A structure well-suited for adverse weather removal by separating and processing long-range and localized information based on SSM.\n\nWeaknesses\n1. The explanation of the architecture is not clear. The relationship between Fig. 4 and Fig. 5 is not clearly articulated in conjunction with the text.\n2. The ablation analysis is insufficient. To convincingly demonstrate the effectiveness of the proposed module, a more detailed ablation study is necessary. This should also be extended to the loss terms.\n3. Lack of visualizations for real world scenarios. (e.g. fog, rain)\n\nQUESTIONS:\n1. Please include more comprehensive ablation studies to validate the effectiveness of DSAM in various settings, such as presenting results when only DDEM and DAFM are used. Additionally, ablations on the loss terms should be conducted. Specifically, it is important to report the model’s performance when knowledge distillation (KD) is removed (i.e., after stage 1), and also to show how well the model performs when trained without ground truth supervision.\n\n2. Visualization results under diverse real-world weather conditions should be provided. Datasets such as RESIDE, NTURain, and RainDS offer a range of real-world adverse weather images that can be used to demonstrate the model’s effectiveness in practical scenarios.\n\n3. Please provide a more detailed explanation of \"HistoFormer+\".\n\n4. Please show the results under different weather conditions in Fig. 3. (rain/snow)\n\n5. The authors should also demonstrate the performance of MODEM on other adverse weather datasets, such as WeatherStream dataset, to validate its generalizability.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper primarily introduces an ordering mechanism based on the Morton-order for images. The proposed Morton-Order 2D Selective Scan Module (MOS2D) combines the Morton ordering with S6 blocks (Selective State Space Model). The paper argues that the proposed ordering scheme better captures the spatial locations of pixels when transforming to the 1D sequence which is fed to the S6 block. The end application is restoring images afflicted by adverse weather such as rain, haze and snow.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n**S1.** The paper is well-written and straight-forward to follow.\n\n**S2.** The proposed method scores strong results on the adverse weather dataset, and performs efficiently (although measured on 256x256 resolution input images).\n\n**Weaknesses**\n\n**W1.** One of the core claims of this work is that the proposed ordering scheme is better at preserving spatial locations when transforming 2D images to 1D sequence. However, there is no comparison with a single Mamba-style restoration network in Tabs. 1-4. There exist many works that explore how well SSMs perform in image restoration problems, many of which the paper already cites in the related work, but does not compare to. This is crucial to understand if indeed the proposed ordering mechanism is better than the prior mechanisms such as OSS in [1], or SS2D in [2], etc.\n\n[1] Shi, Yuan, et al. \"Vmambair: Visual state space model for image restoration.\" IEEE Transactions on Circuits and Systems for Video Technology (2025).\n\n[2] Liu, Yue, et al. \"Vmamba: Visual state space model.\" Advances in neural information processing systems 37 (2024): 103031-103063.\n\n**W2.** Have the authors thought about discontinuities that exist in the scanning order? In Fig. 1, this is visible in (d) when once the first 4 columns and first 4 rows are complete, the next one is col 5, row 1 pixel which is not spatially next to the last pixel that was scanned. Due to this, it is necessary to understand why the proposed scheme is better and why zigzag like scheme [1] or Hilbert curve based scanning [2] is not better?\n\n[1] Hu, Vincent Tao, et al. \"Zigma: A dit-style zigzag mamba diffusion model.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.\n\n[2] Jeong, Min Wu, and Chae Eun Rhee. \"LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n**W3.** Since the training setup is a 2-stage pipeline, I assume the entire model (along with DDEM) is trained given the loss in eq.11. If so, during stage 2, does the restoration backbone now start from scratch or the stage 1 acts as pretraining for the restoration method? In the latter stage (not starting from scratch), there is an issue with the GT information leaking as priors $Z_{0}$ and $Z_{1}$ into the main restoration network.\n\nQUESTIONS:\nOther than the questions listed in weaknesses, a few minor questions are:\n\n**Q1.** How is Fig. 7 generated? Are the features taken from the latent or are they priors?\n\n**Q2.** After pixel at (0,0) and (0,1) are scanned, the next is at (1,0) and not (0,2), making a Z curve along this specific path. Have the authors considered combining multiple scan paths?\n\n**Q3.** Citation of the original Morton order paper is missing [1].\n\n[1] Morton, Guy M. \"A computer oriented geodetic data base and a new technique in file sequencing.\" (1966).\n\n**Comments**\n\nOrdering schemes like Morton-order have been explored in the literature in context of point clouds [1] and now even for image restoration [2], the authors should consider citing these works. Although the latter is fairly recent and can be considered contemporary to this work.\n\n[1] Chen, Guangyan, et al. \"Pointgpt: Auto-regressively generative pre-training from point clouds.\" Advances in Neural Information Processing Systems 36 (2023): 29667-29679.\n\n[2] Ouyang, Zongzhi, and Wenhui Li. \"MMamba: Enhancing image deraining with Morton curve-driven locality learning.\" Neurocomputing 638 (2025): 130161.\n\nLIMITATIONS:\nYes, the authors discuss the limitations of their work, but no discussion on the societal impact is provided.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}