Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising

arXiv:2403.12382v1 Announce Type: cross
Abstract: 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 …

abstract arxiv blind cs.cv cs.lg deep learning denoising development eess.iv focus gap however image images low networks performance training truth type zero-shot