Real-world single image super-resolution: A brief review
Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …
image from a low-resolution (LR) observation, has been an active research topic in the area …
A comprehensive review of deep learning-based real-world image restoration
Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric
Numerous single image super-resolution (SISR) algorithms have been proposed during the
past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) …
past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) …
AND: Adversarial neural degradation for learning blind image super-resolution
Learnt deep neural networks for image super-resolution fail easily if the assumed
degradation model in training mismatches that of the real degradation source at the …
degradation model in training mismatches that of the real degradation source at the …
[HTML][HTML] A real-world benchmark for Sentinel-2 multi-image super-resolution
Insufficient image spatial resolution is a serious limitation in many practical scenarios,
especially when acquiring images at a finer scale is infeasible or brings higher costs. This is …
especially when acquiring images at a finer scale is infeasible or brings higher costs. This is …
Real-rawvsr: Real-world raw video super-resolution with a benchmark dataset
In recent years, real image super-resolution (SR) has achieved promising results due to the
development of SR datasets and corresponding real SR methods. In contrast, the field of …
development of SR datasets and corresponding real SR methods. In contrast, the field of …
Cross-sensor remote sensing imagery super-resolution via an edge-guided attention-based network
The deep learning based super-resolution (SR) methods have recently achieved
remarkable progress in the reconstruction of ideally simulated high-quality remote sensing …
remarkable progress in the reconstruction of ideally simulated high-quality remote sensing …
Learning raw-to-srgb mappings with inaccurately aligned supervision
Learning RAW-to-sRGB mapping has drawn increasing attention in recent years, wherein
an input raw image is trained to imitate the target sRGB image captured by another camera …
an input raw image is trained to imitate the target sRGB image captured by another camera …
Metaf2n: Blind image super-resolution by learning efficient model adaptation from faces
Due to their highly structured characteristics, faces are easier to recover than natural scenes
for blind image super-resolution. Therefore, we can extract the degradation representation of …
for blind image super-resolution. Therefore, we can extract the degradation representation of …
Real‐world super‐resolution of face‐images from surveillance cameras
A Aakerberg, K Nasrollahi… - IET Image Processing, 2022 - Wiley Online Library
Most existing face image Super‐Resolution (SR) methods assume that the Low‐Resolution
(LR) images were artificially downsampled from High‐Resolution (HR) images with bicubic …
(LR) images were artificially downsampled from High‐Resolution (HR) images with bicubic …