Regional Attention for Shadow Removal
- URL: http://arxiv.org/abs/2411.14201v1
- Date: Thu, 21 Nov 2024 15:10:44 GMT
- Title: Regional Attention for Shadow Removal
- Authors: Hengxing Liu, Mingjia Li, Xiaojie Guo,
- Abstract summary: This work devises a lightweight yet accurate shadow removal framework.
We analyze the characteristics of the shadow removal task and design a novel regional attention mechanism.
Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas.
- Score: 10.575174563308046
- License:
- Abstract: Shadow, as a natural consequence of light interacting with objects, plays a crucial role in shaping the aesthetics of an image, which however also impairs the content visibility and overall visual quality. Recent shadow removal approaches employ the mechanism of attention, due to its effectiveness, as a key component. However, they often suffer from two issues including large model size and high computational complexity for practical use. To address these shortcomings, this work devises a lightweight yet accurate shadow removal framework. First, we analyze the characteristics of the shadow removal task to seek the key information required for reconstructing shadow regions and designing a novel regional attention mechanism to effectively capture such information. Then, we customize a Regional Attention Shadow Removal Model (RASM, in short), which leverages non-shadow areas to assist in restoring shadow ones. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas, for seeking the regional contextual correlation between shadow and non-shadow areas. Extensive experiments are conducted to demonstrate that our proposed method delivers superior performance over other state-of-the-art models in terms of accuracy and efficiency, making it appealing for practical applications.
Related papers
- Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey [78.84004293081631]
The patterns of shadows are arbitrary, varied, and often have highly complex trace structures.
The degradation caused by shadows is spatially non-uniform, resulting in inconsistencies in illumination and color between shadow and non-shadow areas.
Recent developments in this field are primarily driven by deep learning-based solutions.
arXiv Detail & Related papers (2024-07-11T20:58:38Z) - Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks [30.071670081122203]
Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination.
Recent deep learning techniques have demonstrated impressive performance in image shadow removal, but their robustness against adversarial attacks remains largely unexplored.
We propose a novel approach, called shadow-adaptive adversarial attack. Different from standard adversarial attacks, our attack budget is adjusted based on the pixel intensity in different regions of shadow images.
arXiv Detail & Related papers (2024-03-15T07:43:42Z) - 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) - Learning Physical-Spatio-Temporal Features for Video Shadow Removal [42.95422940263425]
We propose the first data-driven video shadow removal model, termedNet, by exploiting three essential characteristics of video shadows.
Specifically, dedicated physical branch was established to conduct local illumination estimation, which is more applicable for scenes with complex lighting textures.
To tackle the lack of datasets paired of shadow videos, we synthesize a dataset with aid of the popular game GTAV by controlling the switch of the shadow.
arXiv Detail & Related papers (2023-03-16T14:55:31Z) - 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) - 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) - ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document
Shadow Removal [53.01990632289937]
We propose a Transformer-based model for document shadow removal.
It uses shadow context encoding and decoding in both shadow and shadow-free regions.
arXiv Detail & Related papers (2022-11-30T01:46:29Z) - 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) - 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) - Self-Supervised Shadow Removal [130.6657167667636]
We propose an unsupervised single image shadow removal solution via self-supervised learning by using a conditioned mask.
In contrast to existing literature, we do not require paired shadowed and shadow-free images, instead we rely on self-supervision and jointly learn deep models to remove and add shadows to images.
arXiv Detail & Related papers (2020-10-22T11:33:41Z)
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.