Authors
Zifeng Wang, Yuchen Hong, Tianyi Xu, Haofeng Zhong, Shuchen Weng, Jinxiu Liang, Boxin Shi
Published in
IEEE transactions on pattern analysis and machine intelligence. Volume PP. Sep 07, 2026. Epub Sep 07, 2026.
Abstract
Single image reflection removal (SIRR) is a highly ill-posed task. While recent research explores RAW data for physical accuracy, the dominance of processed (8-bit) images in real-world scenarios necessitates robust solutions capable of handling non-linear degradations. Prevailing methods, however, can struggle on such data due to the lack of high-level semantic guidance. Although language-guided diffusion models show promise in bridging this gap, they face challenges in semantic-spatial alignment and recovery faithfulness due to contaminated input conditions. To address these challenges, this paper introduces RefMover, a multi-instruction diffusion framework for SIRR. It bridges the semantic-spatial gap by combining language and region guidance. To improve faithful and stable recovery, RefMover features a pipeline with a streamlined conditional architecture that mitigates condition conflicts and preserves details. We also introduce a new large-scale dataset (ComRR) for robust training and evaluation. The experimental results demonstrate the effectiveness of the proposed method and its potential for practical interactive and cloud-assisted applications.
PMID:
42704805
Bibliographic data and abstract were imported from PubMed on 08 Sep 2026.
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