We show that raw degradation features can effectively guide deep restoration models,
providing accurate degradation priors to facilitate better restoration. While networks that do …

Vision-language models such as CLIP have shown great impact on diverse downstream
tasks for zero-shot or label-free predictions. However, when it comes to low-level vision such …

Recently, very deep convolutional neural networks (CNNs) have been attracting
considerable attention in image restoration. However, as the depth grows, the long-term …

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 …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

Deep neural networks have exhibited promising performance in image super-resolution
(SR) by learning a nonlinear mapping function from low-resolution (LR) images to high …

Learning to leverage the relationship among diverse image restoration tasks is quite
beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have …

Runtime and memory consumption are two important aspects for efficient image super-
resolution (EISR) models to be deployed on resource-constrained devices. Recent …

Learning continuous image representations is recently gaining popularity for image super-
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Besides dealing with this long-standing task in the spatial domain, a few approaches seek …