Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

Abstract Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision
aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional …

Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-
quality (HQ) images from degraded images achieving promising performance. Due to the …

Compared to single-modality magnetic resonance (MR) image super-resolution (SR)
methods, multi-modality MR image methods can utilize high-resolution reference modality …

Poor light conditions constrain the high pursuit of clarity and visible quality of photography
especially smartphone devices. Admittedly, existing specific image processing methods …

Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in
generating high-quality images. However these methods encounter several challenges …

Abstract The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural
Representation (INR) based works have achieved remarkable success in Arbitrary-Scale …

Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on
simulated data generated by simple synthetic degradation models (eg bicubic …

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging,
providing new opportunities for tissue monitoring and characterization. With the …

Recently, several arbitrary-scale models have been proposed for single-image super-
resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is …