D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal

• URL: http://arxiv.org/abs/2508.12750v3
• Date: Thu, 25 Sep 2025 14:25:16 GMT
• Title: D2-Mamba: Dual-Scale Fusion and Dual-Path Scanning with SSMs for Shadow Removal
• Authors: Linhao Li, Boya Jin, Zizhe Li, Lanqing Guo, Hao Cheng, Bo Li, Yongfeng Dong,
• Abstract summary: We propose a novel Mamba-based network featuring dual-scale fusion and dual-path scanning.<n>We show that our method significantly outperforms existing state-of-the-art approaches on shadow removal benchmarks.
• Score: 20.751391928260563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shadow removal aims to restore images that are partially degraded by shadows, where the degradation is spatially localized and non-uniform. Unlike general restoration tasks that assume global degradation, shadow removal can leverage abundant information from non-shadow regions for guidance. However, the transformation required to correct shadowed areas often differs significantly from that of well-lit regions, making it challenging to apply uniform correction strategies. This necessitates the effective integration of non-local contextual cues and adaptive modeling of region-specific transformations. To this end, we propose a novel Mamba-based network featuring dual-scale fusion and dual-path scanning to selectively propagate contextual information based on transformation similarity across regions. Specifically, the proposed Dual-Scale Fusion Mamba Block (DFMB) enhances multi-scale feature representation by fusing original features with low-resolution features, effectively reducing boundary artifacts. The Dual-Path Mamba Group (DPMG) captures global features via horizontal scanning and incorporates a mask-aware adaptive scanning strategy, which improves structural continuity and fine-grained region modeling. Experimental results demonstrate that our method significantly outperforms existing state-of-the-art approaches on shadow removal benchmarks.

Related papers

• DeshadowMamba: Deshadowing as 1D Sequential Similarity [85.07259906446588]
  We introduce Mamba, a selective state space model that propagates global context through directional state transitions.<n>Despite its potential, directly applying Mamba to image data is suboptimal, since it lacks awareness of shadow-non-shadow semantics.<n>We propose CrossGate, a directional modulation mechanism that injects shadow-aware similarity into Mamba's input gate.<n>To further ensure appearance fidelity, we introduce ColorShift regularization, a contrastive learning objective driven by global color statistics.
  arXiv  Detail & Related papers  (2025-10-28T10:14:23Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network For Salient Object Detection in Optical Remote Sensing Images [60.296124001189646]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement [52.15627062770557]
  Malicious image manipulation poses societal risks, increasing the importance of effective image manipulation detection methods.<n>Recent approaches in image manipulation detection have largely been driven by fully supervised approaches.<n>We present a novel weakly supervised framework based on a dual-branch Transformer-CNN architecture.
  arXiv  Detail & Related papers  (2025-03-26T07:35:09Z)
• 2DMCG:2DMambawith Change Flow Guidance for Change Detection in Remote Sensing [4.18306618346671]
  This paper proposes an efficient framework based on a Vision Mamba variant that enhances its ability to capture 2D spatial information.<n>The framework employs a 2DMamba encoder to effectively learn global contextual spatial information from multi-temporal images.<n>Experiments on benchmark datasets demonstrate the superior performance of our framework compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-01T14:55:13Z)
• Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging [40.80197280147993]
  We propose a Mamba-inspired Joint Unfolding Network (MiJUN) to overcome the inherent nonlinear and ill-posed characteristics of HSI reconstruction.<n>We introduce an accelerated unfolding network scheme, which reduces the reliance on initial optimization stages.<n>We refine the scanning strategy with Mamba by integrating the tensor mode-$k$ unfolding into the Mamba network.
  arXiv  Detail & Related papers  (2025-01-02T13:56:23Z)
• ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal [3.5734732877967392]
  This paper presents a model called ShadowMamba, the first Mamba-based model designed for shadow removal.<n> Experimental results show that the proposed method outperforms existing mainstream approaches on the AISTD, ISTD, and SRD datasets.
  arXiv  Detail & Related papers  (2024-11-05T16:59:06Z)
• Diff-Shadow: Global-guided Diffusion Model for Shadow Removal [46.41983327564438]
  We propose Diff-Shadow, a global-guided diffusion model for shadow removal.<n>We combine the advantages of diffusion models and global guidance to achieve shadow-free restoration.<n>Our method achieves a significant improvement in terms of PSNR, increasing from 32.33dB to 33.69dB on the ISTD dataset.
  arXiv  Detail & Related papers  (2024-07-23T06:42:55Z)
• SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization [67.90315365909244]
  We propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper. Our method generates detailed makeup transfer results that can handle large spatial misalignments and achieve part-specific and shade-controllable makeup transfer. Experimental results show that our SARA method outperforms existing methods and achieves state-of-the-art performance on two public datasets.
  arXiv  Detail & Related papers  (2023-11-28T14:46:51Z)
• Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal [19.391619888009064]
  State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs. We find that two tasks exhibit poor compatibility, and using shared weights for these two tasks could lead to the model being optimized towards only one task. We propose to handle these two tasks separately and leverage the identical mapping results to guide the shadow restoration in an iterative manner.
  arXiv  Detail & Related papers  (2023-05-18T01:36:23Z)
• PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening [50.943080184828524]
  We propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously.
  arXiv  Detail & Related papers  (2022-07-29T03:09:21Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.