DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal
- URL: http://arxiv.org/abs/2307.15318v2
- Date: Tue, 30 Jul 2024 04:27:26 GMT
- Title: DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal
- Authors: Ziyang Zhou, Yingtie Lei, Xuhang Chen, Shenghong Luo, Wenjun Zhang, Chi-Man Pun, Zhen Wang,
- Abstract summary: Current shadow removal techniques face limitations in handling varying shadow intensities and preserving document details.
We propose DocDeshadower, a novel multi-frequency Transformer-based model built upon the Laplacian Pyramid.
Experiments demonstrate DocDeshadower's superior performance compared to state-of-the-art methods.
- Score: 36.182923899021496
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Shadows in scanned documents pose significant challenges for document analysis and recognition tasks due to their negative impact on visual quality and readability. Current shadow removal techniques, including traditional methods and deep learning approaches, face limitations in handling varying shadow intensities and preserving document details. To address these issues, we propose DocDeshadower, a novel multi-frequency Transformer-based model built upon the Laplacian Pyramid. By decomposing the shadow image into multiple frequency bands and employing two critical modules: the Attention-Aggregation Network for low-frequency shadow removal and the Gated Multi-scale Fusion Transformer for global refinement. DocDeshadower effectively removes shadows at different scales while preserving document content. Extensive experiments demonstrate DocDeshadower's superior performance compared to state-of-the-art methods, highlighting its potential to significantly improve document shadow removal techniques. The code is available at https://github.com/leiyingtie/DocDeshadower.
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