Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising
<|reference_start|>Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising: Deep learning-based denoiser has been the focus of recent development on image denoising. In the past few years, there has been increasing interest in developing self-supervised denoising networks that only require noisy images, without the need for clean ground truth for training. However, a performance gap remains between current self-supervised methods and their supervised counterparts. Additionally, these methods commonly depend on assumptions about noise characteristics, thereby constraining their applicability in real-world scenarios. Inspired by the properties of the Frobenius norm expansion, we discover that incorporating a trace term reduces the optimization goal disparity between self-supervised and supervised methods, thereby enhancing the performance of self-supervised learning. To exploit this insight, we propose a trace-constraint loss function and design the low-trace adaptation Noise2Noise (LoTA-N2N) model that bridges the gap between self-supervised and supervised learning. Furthermore, we have discovered that several existing self-supervised denoising frameworks naturally fall within the proposed trace-constraint loss as subcases. Extensive experiments conducted on natural and confocal image datasets indicate that our method achieves state-of-the-art performance within the realm of zero-shot self-supervised image denoising approaches, without relying on any assumptions regarding the noise.<|reference_end|>
@article{hu2024low-trace,
    title={Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising},
    author={Jintong Hu, Bin Xia, Bingchen Li, Wenming Yang},
    journal={arXiv preprint arXiv:2403.12382},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12382},
    primaryClass={eess.IV cs.CV cs.LG}
}