Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …

Abstract Low-light Image Enhancement (LIE) aims at improving contrast and restoring
details for images captured in low-light conditions. Most of the previous LIE algorithms adjust …

Recent advances in deep learning have been pushing image denoising techniques to a
new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most …

Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile,
supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised …

Blind-spot network (BSN) and its variants have made significant advances in self-supervised
denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical …

In recent years, the development of deep learning has been pushing image denoising to a
new level. Among them, self-supervised denoising is increasingly popular because it does …

Significant progress has been made in self-supervised image denoising (SSID) in the recent
few years. However, most methods focus on dealing with spatially independent noise, and …

Seismic image denoising is essential to enhance the signal-to-noise ratio (SNR) of seismic
images and facilitate seismic processing and geological structure interpretation. With the …