Single-image reflection removal via self-supervised diffusion models

• URL: http://arxiv.org/abs/2412.20466v1
• Date: Sun, 29 Dec 2024 13:41:33 GMT
• Title: Single-image reflection removal via self-supervised diffusion models
• Authors: Zhengyang Lu, Weifan Wang, Tianhao Guo, Feng Wang,
• Abstract summary: This paper proposes a hybrid approach that combines cycle-consistency with denoising diffusion probabilistic models (DDPM) to remove reflections from single images without requiring paired training data. Experimental results demonstrate the effectiveness of the proposed method on the SIR$2$, Flash-Based Reflection Removal (FRR) dataset, and a newly introduced Museum Reflection Removal (MRR) dataset.
• Score: 2.3838561104233342
• License:
• Abstract: Reflections often degrade the visual quality of images captured through transparent surfaces, and reflection removal methods suffers from the shortage of paired real-world samples.This paper proposes a hybrid approach that combines cycle-consistency with denoising diffusion probabilistic models (DDPM) to effectively remove reflections from single images without requiring paired training data. The method introduces a Reflective Removal Network (RRN) that leverages DDPMs to model the decomposition process and recover the transmission image, and a Reflective Synthesis Network (RSN) that re-synthesizes the input image using the separated components through a nonlinear attention-based mechanism. Experimental results demonstrate the effectiveness of the proposed method on the SIR$^2$, Flash-Based Reflection Removal (FRR) Dataset, and a newly introduced Museum Reflection Removal (MRR) dataset, showing superior performance compared to state-of-the-art methods.

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