Single image super-resolution via adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization
Single image super-resolution (SISR) is a challenging work, which aims to recover the
missing information in an observed low-resolution (LR) image and generate the …
missing information in an observed low-resolution (LR) image and generate the …
Survey on Single Image based Super-resolution — Implementation Challenges and Solutions
A Singh, J Singh - Multimedia Tools and Applications, 2020 - Springer
Super-resolution includes the techniques which deal with the methods of converting the low-
resolution image into the high-resolution image. In this paper, various challenges affecting …
resolution image into the high-resolution image. In this paper, various challenges affecting …
Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis
Image super-resolution aims to generate high-resolution image based on the given low-
resolution image and to recover the details of the image. The common approaches include …
resolution image and to recover the details of the image. The common approaches include …
Single image super-resolution using feature adaptive learning and global structure sparsity
J Liu, Y Liu, H Wu, J Wang, X Li, C Zhang - Signal Processing, 2021 - Elsevier
Due to the important application value of image super-resolution, many image super-
resolution algorithms have been proposed in recent years. However, many single-image …
resolution algorithms have been proposed in recent years. However, many single-image …
Enhanced non-local total variation model and multi-directional feature prediction prior for single image super resolution
It is widely acknowledged that single image super-resolution (SISR) methods play a critical
role in recovering the missing high-frequencies in an input low-resolution image. As SISR is …
role in recovering the missing high-frequencies in an input low-resolution image. As SISR is …
SRHRF+: Self-example enhanced single image super-resolution using hierarchical random forests
Example-based single image super-resolution (SISR) methods use external training
datasets and have recently attracted a lot of interest. Self-example based SISR methods …
datasets and have recently attracted a lot of interest. Self-example based SISR methods …
Weighted adaptive image super-resolution scheme based on local fractal feature and image roughness
Image super-resolution aims to reconstruct a high-resolution image from the known low-
resolution version. During this process, it should keep the degree of image roughness non …
resolution version. During this process, it should keep the degree of image roughness non …
Structure preserving loss function for single image super resolution
The single image super-resolution (SISR) is a challenging problem due to its ill-posed
nature. The main aim of SISR methods is to generate a high-resolution image from a given …
nature. The main aim of SISR methods is to generate a high-resolution image from a given …
Single image super-resolution incorporating example-based gradient profile estimation and weighted adaptive p-norm
Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from only
one observed low-resolution (LR) image. It is a severely ill-posed problem that needs image …
one observed low-resolution (LR) image. It is a severely ill-posed problem that needs image …
Performance analysis of JPEG XR with deep learning-based image super-resolution
T Min, S Aramvith - 2022 Asia-Pacific Signal and Information …, 2022 - ieeexplore.ieee.org
The demand for efficient and high-level image and video codec compression has been
widely increasing. Conventional image compression methods such as JPEG XR use a high …
widely increasing. Conventional image compression methods such as JPEG XR use a high …