Image Reflection Separation via Adaptive Residual Correction and Feature Interaction Enhancement

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dc.contributor.authorKe, Weijianen_US
dc.contributor.authorMo, Yijunen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:03:30Z
dc.date.available2025-10-07T06:03:30Z
dc.date.issued2025
dc.description.abstractGlass reflection superimposes images from both sides of the glass, resulting in severe image quality degradation that significantly impairs the performance of downstream tasks, such as object detection and image understanding. Therefore, it is essential to separate the transmission and reflection layers. However, due to lighting conditions and the material properties of glass, the relationship between the reflected and transmitted components often involves complex linear interactions, which limit the effectiveness of existing methods. Inspired by the observation that transmission components often dominate images with reflection in real-world scenes, we propose an image reflection separation method that integrates adaptive residual correction with feature interaction enhancement. Building upon a linear combination model enhanced with residual correction, we generalize the residual term based on the physical principles of light reflection and transmission. In order to ensure precise spatial alignment between the transparent and real images, We design an image registration mechanism and propose an Adaptive Hybrid Residual Loss, which significantly enhances the model's ability to perceive differences between the transmission and reflection layers, effectively balancing the complexity of linear mixture modeling with the diversity of real-world scenarios. To further highlight the interactive features between reflection and transmission, we incorporate a cross-dimensional attention mechanism into the dual-stream architecture designed for transmission-reflection processing. Extensive experiments and ablation studies show that our method achieves state-of-the-art performance on multiple real-world benchmark datasets, with an average PSNR improvement of 0.66 dB over the current best-performing model.en_US
dc.description.sectionheadersEnhancing Images
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251275
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251275
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251275
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computational photography; Computer vision; Image manipulation
dc.subjectComputing methodologies → Computational photography
dc.subjectComputer vision
dc.subjectImage manipulation
dc.titleImage Reflection Separation via Adaptive Residual Correction and Feature Interaction Enhancementen_US
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