Single image super-resolution (SISR) is an important research field in computer vision, the
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

Transformer-based methods have exhibited remarkable potential in single image super-
resolution (SISR) by effectively extracting long-range dependencies. However, most of the …

Multi-scale feature extraction is crucial for many computer vision tasks, but it is rarely
explored in Transformer-based image super-resolution (SR) methods. In this paper, we …

Underwater image enhancement presents a significant challenge due to the complex and
diverse underwater environments that result in severe degradation phenomena such as light …

Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from
its corresponding low-resolution (LR) input image with unknown degradations. Most of the …

Many recent image restoration methods use Transformer as the backbone network and
redesign the Transformer blocks. Differently, we explore the parameter-sharing mechanism …

Depth prediction is at the core of several computer vision applications, such as autonomous
driving and robotics. It is often formulated as a regression task in which depth values are …