Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially
invariant across the whole image. However, such an assumption is rarely applicable for real …

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs)
in image restoration. However, a critical limitation is that they cannot generalize well to real …

Single image super resolution (SISR) is an important research content in the field of
computer vision and image processing. With the rapid development of deep neural …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

Unpaired image super-resolution (SR) has recently attracted considerable attention in the
unsupervised SR community. In contrast to supervised SR, existing unpaired SR methods …

Diffusion models have gained significant popularity for image-to-image translation tasks.
Previous efforts applying diffusion models to image super-resolution have demonstrated that …

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-
cycled Cycle-GAN architecture is widely employed to achieve promising performance on …

With widespread applications of artificial intelligence (AI), the capabilities of the perception,
understanding, decision-making, and control for autonomous systems have improved …

Blind face restoration focuses on restoring high-fidelity details from images subjected to
complex and unknown degradations while preserving identity information. In this paper we …

Video super-resolution aims to recover the high-resolution (HR) contents from the low-
resolution (LR) observations relying on compositing the spatial-temporal information in the …