Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions

• URL: http://arxiv.org/abs/2605.24590v1
• Date: Sat, 23 May 2026 14:03:42 GMT
• Title: Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions
• Abstract summary: Low-light, long-exposure deblurring remains a challenging problem due to the simultaneous presence of severe blur and complex biased noise.<n>Existing methods typically rely on simplified noise assumptions, which limits their effectiveness under realistic imaging conditions.<n>We propose Physen-Noise2Noise, a self-supervised deblurring framework guided by the physical model of defocus imaging.
• Score: 11.6067553207333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light, long-exposure defocus deblurring remains a challenging problem due to the simultaneous presence of severe blur and complex biased noise. Existing methods typically rely on simplified noise assumptions, which limits their effectiveness under realistic imaging conditions. In this work, we propose Physen-Noise2Noise, a self-supervised deblurring framework guided by the physical model of defocus imaging, which leverages noisy multi-frame observations without requiring clean reference images. Unlike conventional Noise2Noise-based approaches that assume zero-mean noise, we derive a frequency-domain constraint inherent to the defocus imaging process and incorporate it into the learning framework via a learnable noise bias parameter. In addition, a multi-frame noisy initialization strategy is introduced to suppress complex biased noise prior to deblurring, providing a more stable starting point for reconstruction. This formulation explicitly models biased noise and enables joint bias correction and high-frequency detail recovery during training. Furthermore, we develop a pretrain-finetune variant to enhance robustness and generalization under challenging noise conditions. Extensive experiments on both simulation and real-world datasets demonstrate that the proposed method consistently outperforms state-of-the-art self-supervised approaches for defocus deblurring in the presence of complex biased noise.

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