This paper reviews the NTIRE 2024 challenge on image super-resolution (x4) highlighting
the solutions proposed and the outcomes obtained. The challenge involves generating …

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

For medical image analysis, there is always an immense need for rich details in an image.
Typically, the diagnosis will be served best if the fine details in the image are retained and …

Deep convolution-based single image super-resolution (SISR) networks embrace the
benefits of learning from large-scale external image resources for local recovery, yet most …

In this letter, we present a conditional generative adversarial network-based model for real-
time underwater image enhancement. To supervise the adversarial training, we formulate an …

Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and
4K (UHD) resolution have become standard. While higher resolutions offer improved visual …

Stereo image pairs can be used to improve the performance of super-resolution (SR) since
additional information is provided from a second viewpoint. However, it is challenging to …

Video super-resolution (VSR) aims to utilize multiple low-resolution frames to generate a
high-resolution prediction for each frame. In this process, inter-and intra-frames are the key …