Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref)
images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting …

Abstract We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-
NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis …

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …

Magnetic resonance imaging (MRI) can present multi-contrast images of the same
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical
image into a high-quality (HQ) one without relying on paired images for training. While most …

One key challenge of exemplar-guided image generation lies in establishing fine-grained
correspondences between input and guided images. Prior approaches, despite the …

Generative models operate at fixed resolution, even though natural images come in a variety
of sizes. As high-resolution details are downsampled away and low-resolution images are …

Self-similarity is valuable to the exploration of non-local textures in single image super-
resolution (SISR). Researchers usually assume that the importance of non-local textures is …

Existing super-resolution (SR) models primarily focus on restoring local texture details often
neglecting the global semantic information within the scene. This oversight can lead to the …