We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …

We study on image super-resolution (SR), which aims to recover realistic textures from a low-
resolution (LR) image. Recent progress has been made by taking high-resolution images as …

Recent state-of-the-art super-resolution methods have achieved impressive performance on
ideal datasets regardless of blur and noise. However, these methods always fail in real …

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 …

Small object detection remains an unsolved challenge because it is hard to extract the
information of small objects with only a few pixels. While scale-level corresponding detection …

Reference-based image super-resolution (RefSR) has shown promising success in
recovering high-frequency details by utilizing an external reference image (Ref). In this task …

Due to the significant information loss in low-resolution (LR) images, it has become
extremely challenging to further advance the state-of-the-art of single image super …

Light fields are vector functions that map the geometry of light rays to the corresponding
plenoptic attributes. They describe the holographic information of scenes by representing …

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