CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal

• URL: http://arxiv.org/abs/2209.02174v1
• Date: Tue, 6 Sep 2022 01:33:38 GMT
• Title: CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
• Authors: Qianhao Yu, Naishan Zheng, Jie Huang, Feng Zhao
• Abstract summary: We propose a shadow-oriented adaptive normalization (SOAN) module and a shadow-aware aggregation with transformer (SAAT) module based on the shadow mask. Under the guidance of the shadow mask, the SOAN module formulates the statistics from the non-shadow region and adaptively applies them to the shadow region for region-wise restoration. The SAAT module utilizes the shadow mask to precisely guide the restoration of each shadowed pixel by considering the highly relevant pixels from the shadow-free regions for global pixel-wise restoration.
• Score: 4.951051823391577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions. Due to the inadequate long-range modeling, the CNN-based approaches cannot thoroughly investigate the information from the non-shadow regions. To solve this problem, we propose a novel cleanness-navigated-shadow network (CNSNet), with a shadow-oriented adaptive normalization (SOAN) module and a shadow-aware aggregation with transformer (SAAT) module based on the shadow mask. Under the guidance of the shadow mask, the SOAN module formulates the statistics from the non-shadow region and adaptively applies them to the shadow region for region-wise restoration. The SAAT module utilizes the shadow mask to precisely guide the restoration of each shadowed pixel by considering the highly relevant pixels from the shadow-free regions for global pixel-wise restoration. Extensive experiments on three benchmark datasets (ISTD, ISTD+, and SRD) show that our method achieves superior de-shadowing performance.

Related papers

• ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal [3.5734732877967392]
  This paper proposes a novel selective scanning method called boundary-region selective scanning. We name our model ShadowMamba, the first Mamba-based model for shadow removal.
  arXiv  Detail & Related papers  (2024-11-05T16:59:06Z)
• Shadow Removal Refinement via Material-Consistent Shadow Edges [33.8383848078524]
  On both sides of shadow edges traversing regions with the same material, the original color and textures should be the same if the shadow is removed properly. We fine-tune SAM, an image segmentation foundation model, to produce a shadow-invariant segmentation and then extract material-consistent shadow edges. We demonstrate the effectiveness of our method in improving shadow removal results on more challenging, in-the-wild images.
  arXiv  Detail & Related papers  (2024-09-10T20:16:28Z)
• SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection [90.4751446041017]
  We present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The whole process can be divided into three parts: encoder, decoder, and feature integration. Experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER)
  arXiv  Detail & Related papers  (2024-08-07T03:16:33Z)
• Progressive Recurrent Network for Shadow Removal [99.1928825224358]
  Single-image shadow removal is a significant task that is still unresolved. Most existing deep learning-based approaches attempt to remove the shadow directly, which can not deal with the shadow well. We propose a simple but effective Progressive Recurrent Network (PRNet) to remove the shadow progressively.
  arXiv  Detail & Related papers  (2023-11-01T11:42:45Z)
• 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)
• ShadowFormer: Global Context Helps Image Shadow Removal [41.742799378751364]
  It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions. We first propose a Retinex-based shadow model, from which we derive a novel transformer-based network, dubbed ShandowFormer. A multi-scale channel attention framework is employed to hierarchically capture the global information. We propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions.
  arXiv  Detail & Related papers  (2023-02-03T10:54:52Z)
• Structure-Informed Shadow Removal Networks [67.57092870994029]
  Existing deep learning-based shadow removal methods still produce images with shadow remnants. We propose a novel structure-informed shadow removal network (StructNet) to leverage the image-structure information to address the shadow remnant problem. Our method outperforms existing shadow removal methods, and our StructNet can be integrated with existing methods to improve them further.
  arXiv  Detail & Related papers  (2023-01-09T06:31:52Z)
• Shadow Removal by High-Quality Shadow Synthesis [78.56549207362863]
  HQSS employs a shadow feature encoder and a generator to synthesize pseudo images. HQSS is observed to outperform the state-of-the-art methods on ISTD dataset, Video Shadow Removal dataset, and SRD dataset.
  arXiv  Detail & Related papers  (2022-12-08T06:52:52Z)
• Shadow-Aware Dynamic Convolution for Shadow Removal [80.82708225269684]
  We introduce a novel Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module. We develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region.
  arXiv  Detail & Related papers  (2022-05-10T14:00:48Z)
• R2D: Learning Shadow Removal to Enhance Fine-Context Shadow Detection [64.10636296274168]
  Current shadow detection methods perform poorly when detecting shadow regions that are small, unclear or have blurry edges. We propose a new method called Restore to Detect (R2D), where a deep neural network is trained for restoration (shadow removal) We show that our proposed method R2D improves the shadow detection performance while being able to detect fine context better compared to the other recent methods.
  arXiv  Detail & Related papers  (2021-09-20T15:09:22Z)

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.