Retinex-guided Histogram Transformer for Mask-free Shadow Removal

• URL: http://arxiv.org/abs/2504.14092v1
• Date: Fri, 18 Apr 2025 22:19:40 GMT
• Title: Retinex-guided Histogram Transformer for Mask-free Shadow Removal
• Authors: Wei Dong, Han Zhou, Seyed Amirreza Mousavi, Jun Chen,
• Abstract summary: ReHiT is an efficient mask-free shadow removal framework based on a hybrid CNN-Transformer architecture guided by Retinex theory.<n>Our solution delivers competitive results with one of the smallest parameter sizes and fastest inference speeds among top-ranked entries.
• Score: 12.962534359029103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While deep learning methods have achieved notable progress in shadow removal, many existing approaches rely on shadow masks that are difficult to obtain, limiting their generalization to real-world scenes. In this work, we propose ReHiT, an efficient mask-free shadow removal framework based on a hybrid CNN-Transformer architecture guided by Retinex theory. We first introduce a dual-branch pipeline to separately model reflectance and illumination components, and each is restored by our developed Illumination-Guided Hybrid CNN-Transformer (IG-HCT) module. Second, besides the CNN-based blocks that are capable of learning residual dense features and performing multi-scale semantic fusion, multi-scale semantic fusion, we develop the Illumination-Guided Histogram Transformer Block (IGHB) to effectively handle non-uniform illumination and spatially complex shadows. Extensive experiments on several benchmark datasets validate the effectiveness of our approach over existing mask-free methods. Trained solely on the NTIRE 2025 Shadow Removal Challenge dataset, our solution delivers competitive results with one of the smallest parameter sizes and fastest inference speeds among top-ranked entries, highlighting its applicability for real-world applications with limited computational resources. The code is available at https://github.com/dongw22/oath.

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