Although there have been significant advances in the field of image restoration recently, the
system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

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 …

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …

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 …

When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …

Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …

This work aims to improve the applicability of diffusion models in realistic image restoration.
Specifically, we enhance the diffusion model in several aspects such as network …

Real-world low-light images suffer from two main degradations, namely, inevitable noise
and poor visibility. Since the noise exhibits different levels, its estimation has been …

With the advent of Internet, images and videos are the most vulnerable media that can be
exploited by criminals to manipulate for hiding the evidence of the crime. This is now easier …