Kavli Affiliate: Feng Wang
| First 5 Authors: Zhengyang Lu, Weifan Wang, Tianhao Guo, Feng Wang,
| Summary:
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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