A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal

• URL: http://arxiv.org/abs/2503.01938v1
• Date: Mon, 03 Mar 2025 07:54:27 GMT
• Title: A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal
• Authors: Jun-Jie Huang, Tianrui Liu, Zihan Chen, Xinwang Liu, Meng Wang, Pier Luigi Dragotti,
• Abstract summary: Single Image Reflection Removal (SIRR) is a canonical blind source separation problem.<n>We propose a novel Deep Exclusion unfolding Network (DExNet) for SIRR.<n>DExNet is constructed by unfolding and parameterizing a simple iterative Sparse and Auxiliary Feature Update (i-SAFU) algorithm.
• Score: 68.0573194557999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The core challenge lies in minimizing the commonalities among different sources. Existing deep learning approaches either neglect the significance of feature interactions or rely on heuristically designed architectures. In this paper, we propose a novel Deep Exclusion unfolding Network (DExNet), a lightweight, interpretable, and effective network architecture for SIRR. DExNet is principally constructed by unfolding and parameterizing a simple iterative Sparse and Auxiliary Feature Update (i-SAFU) algorithm, which is specifically designed to solve a new model-based SIRR optimization formulation incorporating a general exclusion prior. This general exclusion prior enables the unfolded SAFU module to inherently identify and penalize commonalities between the transmission and reflection features, ensuring more accurate separation. The principled design of DExNet not only enhances its interpretability but also significantly improves its performance. Comprehensive experiments on four benchmark datasets demonstrate that DExNet achieves state-of-the-art visual and quantitative results while utilizing only approximately 8\% of the parameters required by leading methods.

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