LowAux-RDNet: Low-Pass Residual Supervision with Scene-Balanced Real-World Training for Single-Image Reflection Removal
The paper proposes LowAux-RDNet, a method that introduces a training-only low-pass reflection auxiliary objective (LowAux) on top of RDNet. This low-frequency constraint, combined with scene-balanced real pairs from RRW, improves generalization across diverse reflection distributions. A unified benchmark on five datasets yields macro-average PSNR of 27.546 dB, with SSIM, NCC, and LMSE all reaching state-of-the-art.
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[Submitted on 20 Jul 2026]
Title:LowAux-RDNet: Low-Pass Residual Supervision with Scene-Balanced Real-World Training for Single-Image Reflection Removal
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Abstract:Single-image reflection removal aims to recover a clean transmission layer from one image captured through glass. We study an explicit decomposition pipeline built on RDNet and introduce LowAux, a training-only low-pass reflection auxiliary objective. The original residual target remains the main reflection supervision, while symmetrically filtered prediction and target provide a stable low-frequency constraint. We further incorporate scene-balanced real pairs from RRW to broaden real-scene coverage and improve cross-dataset generalization. To avoid evaluation discrepancies caused by model-specific resizing, padding, output quantization, and metric code, we build a unified public benchmark over CEILNet, Real20, Postcard, Objects, and Wild. Under the same evaluator, the proposed system obtains a five-dataset macro average of 27.546 dB PSNR, 0.9220 SSIM, 0.9751 NCC, and 0.004760 LMSE, achieving the highest macro-average PSNR, SSIM, and NCC and the lowest LMSE among the compared public checkpoints and internal variants. Per-dataset and qualitative analyses show that the main benefit is a more balanced performance across diverse reflection distributions, while clear semantic reflections in Postcard remain challenging.
Comments: 10 pages, 5 figures. Code: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.22707 [cs.CV]
(or arXiv:2607.22707v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22707
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jizhong Li [view email] [v1] Mon, 20 Jul 2026 12:45:16 UTC (2,430 KB)
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