UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal

• URL: http://arxiv.org/abs/2203.15441v2
• Date: Thu, 24 Aug 2023 19:29:18 GMT
• Title: UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
• Authors: Subhrajyoti Dasgupta, Arindam Das, Senthil Yogamani, Sudip Das, Ciaran Eising, Andrei Bursuc and Ujjwal Bhattacharya
• Abstract summary: We introduce a novel weakly supervised shadow removal framework UnShadowNet. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available.
• Score: 14.898039056038789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artefacts. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups.

Related papers

• OmniSR: Shadow Removal under Direct and Indirect Lighting [16.90413085184936]
  A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. We propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms.
  arXiv  Detail & Related papers  (2024-10-02T16:30:10Z)
• ShadowRemovalNet: Efficient Real-Time Shadow Removal [3.0516727053033392]
  ShadowRemovalNet is a novel method for real-time image processing on resource-constrained hardware. It achieves significantly higher frame rates compared to existing methods. It does not require a separate shadow mask during inference.
  arXiv  Detail & Related papers  (2024-03-13T00:04:07Z)
• 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)
• 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)
• 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)
• 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)
• DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network [28.6541488555978]
  We propose an unsupervised domain-classifier guided shadow removal network, DC-ShadowNet. We introduce novel losses based on physics-based shadow-free chromaticity, shadow-robust perceptual features, and boundary smoothness. Our experiments show that all these novel components allow our method to handle soft shadows, and also to perform better on hard shadows.
  arXiv  Detail & Related papers  (2022-07-21T12:04:16Z)
• 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)
• From Shadow Generation to Shadow Removal [19.486543304598264]
  We propose a new G2R-ShadowNet which leverages shadow generation for weakly-supervised shadow removal. The proposed G2R-ShadowNet consists of three sub-networks for shadow generation, shadow removal and refinement. In particular, the shadow generation sub-net stylises non-shadow regions to be shadow ones, leading to paired data for training the shadow-removal sub-net.
  arXiv  Detail & Related papers  (2021-03-24T05:49:08Z)
• 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.