Image super-resolution: A comprehensive review, recent trends, challenges and applications
Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …
processing to improve the visual perception of the poor-quality images. The key objective of …
A deep journey into super-resolution: A survey
Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …
practical applications. In this exposition, we extensively compare more than 30 state-of-the …
Srdiff: Single image super-resolution with diffusion probabilistic models
H Li, Y Yang, M Chang, S Chen, H Feng, Z Xu, Q Li… - Neurocomputing, 2022 - Elsevier
Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from
given low-resolution (LR) images. It is an ill-posed problem because one LR image …
given low-resolution (LR) images. It is an ill-posed problem because one LR image …
Unsupervised degradation representation learning for blind super-resolution
Most existing CNN-based super-resolution (SR) methods are developed based on an
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …
A hybrid network of cnn and transformer for lightweight image super-resolution
J Fang, H Lin, X Chen, K Zeng - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …
super-resolution. However, these existing architectures commonly build massive number of …
Latticenet: Towards lightweight image super-resolution with lattice block
Deep neural networks with a massive number of layers have made a remarkable
breakthrough on single image super-resolution (SR), but sacrifice computation complexity …
breakthrough on single image super-resolution (SR), but sacrifice computation complexity …
Real-world super-resolution via kernel estimation and noise injection
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 …
ideal datasets regardless of blur and noise. However, these methods always fail in real …
Flow-based kernel prior with application to blind super-resolution
Kernel estimation is generally one of the key problems for blind image super-resolution
(SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior …
(SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior …
Densely residual laplacian super-resolution
Super-Resolution convolutional neural networks have recently demonstrated high-quality
restoration for single images. However, existing algorithms often require very deep …
restoration for single images. However, existing algorithms often require very deep …
A review of image super-resolution approaches based on deep learning and applications in remote sensing
At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …
images are becoming more widely used in real scenes. However, due to the limitations of …