Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising
Jintong Hu Bin Xia Bingchen Li Wenming Yang
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.