FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors

• URL: http://arxiv.org/abs/2504.05779v1
• Date: Tue, 08 Apr 2025 08:00:58 GMT
• Title: FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors
• Authors: Tao Lin, Qingwang Wang, Qiwei Liang, Minghua Tang, Yuxuan Sun,
• Abstract summary: unsupervised Frequency Aware Shadow Removal Network (FASR-Net)<n>Wavelet Attention Downsampling Module (WADM)<n>New loss functions for precise shadow-free image reproduction.
• Score: 6.212425737295235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shadow removal is challenging due to the complex interaction of geometry, lighting, and environmental factors. Existing unsupervised methods often overlook shadow-specific priors, leading to incomplete shadow recovery. To address this issue, we propose a novel unsupervised Frequency Aware Shadow Removal Network (FASR-Net), which leverages the inherent frequency characteristics of shadow regions. Specifically, the proposed Wavelet Attention Downsampling Module (WADM) integrates wavelet-based image decomposition and deformable attention, effectively breaking down the image into frequency components to enhance shadow details within specific frequency bands. We also introduce several new loss functions for precise shadow-free image reproduction: a frequency loss to capture image component details, a brightness-chromaticity loss that references the chromaticity of shadow-free regions, and an alignment loss to ensure smooth transitions between shadowed and shadow-free regions. Experimental results on the AISTD and SRD datasets demonstrate that our method achieves superior shadow removal performance.

Related papers

• MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis [64.00425120075045]
  Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion.
  arXiv  Detail & Related papers  (2024-12-03T18:04:42Z)
• Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal [2.999888908665659]
  This study proposes a novel deep learning architecture, named Soft-Hard Attention U-net (SHAU), focusing on multiscale shadow removal. It provides a novel synthetic dataset, named Multiscale Shadow Removal dataset (MSRD), containing complex shadow patterns of multiple scales. The results demonstrate the effectiveness of SHAU over the relevant state-of-the-art shadow removal methods across various benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-07T12:42:06Z)
• Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal [22.4845448174729]
  We propose a weakly supervised shadow removal network with a spherical feature space, dubbed S2-ShadowNet, to explore the best of both worlds for visible and infrared modalities. Specifically, we employ a modal translation (visible-to-infrared) model to learn the cross-domain mapping, thus generating realistic infrared samples. We contribute a large-scale weakly supervised shadow removal benchmark, including 4000 shadow images with corresponding shadow masks.
  arXiv  Detail & Related papers  (2024-06-25T11:14:09Z)
• ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer [41.008740643546226]
  Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination. We introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge.
  arXiv  Detail & Related papers  (2024-04-18T03:53:33Z)
• 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)
• SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection [85.16141353762445]
  We treat the input shadow image as a composition of a background layer and a shadow layer, and design a Style-guided Dual-layer Disentanglement Network to model these layers independently.<n>Our model effectively minimizes the detrimental effects of background color, yielding superior performance on three public datasets with a real-time inference speed of 32 FPS.
  arXiv  Detail & Related papers  (2023-08-17T12:10:51Z)
• 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)
• Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning [66.36104525390316]
  We propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses. To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image.
  arXiv  Detail & Related papers  (2022-11-27T07:26:41Z)
• DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity [54.831083157152136]
  We present a method that removes hard, soft and self shadows based on adaptive attention and ViT similarity. Our method outperforms state-of-the-art methods on the SRD, AISTD, LRSS, USR and UIUC datasets.
  arXiv  Detail & Related papers  (2022-11-15T12:15:29Z)
• CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal [4.951051823391577]
  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.
  arXiv  Detail & Related papers  (2022-09-06T01:33:38Z)
• 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.