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Image super-resolution: The techniques, applications, and future
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …
the observed LR images. As SR has been developed for more than three decades, both …
[HTML][HTML] Regularization-based multi-frame super-resolution: a systematic review
High-resolution is generally required and preferred for producing more detailed information
inside the digital images; therefore, this leads to improve the pictorial information for human …
inside the digital images; therefore, this leads to improve the pictorial information for human …
Toward bridging the simulated-to-real gap: Benchmarking super-resolution on real data
Capturing ground truth data to benchmark super-resolution (SR) is challenging. Therefore,
current quantitative studies are mainly evaluated on simulated data artificially sampled from …
current quantitative studies are mainly evaluated on simulated data artificially sampled from …
Robust multiframe super-resolution employing iteratively re-weighted minimization
Multiframe super-resolution algorithms reconstruct high-resolution images by exploiting
complementary information in multiple low-resolution frames. However, despite their …
complementary information in multiple low-resolution frames. However, despite their …
A new denoising model for multi-frame super-resolution image reconstruction
Multi-frame image super-resolution (SR) aims to combine the sub-pixel information from a
sequence of low-resolution (LR) images to build a high-resolution (HR) one. SR techniques …
sequence of low-resolution (LR) images to build a high-resolution (HR) one. SR techniques …
A regularization by denoising super-resolution method based on genetic algorithms
Increasing the resolution of an image is an actual and extensively studied problem in image
processing. Recently, Regularization by Denoising (RED) showing that any inverse problem …
processing. Recently, Regularization by Denoising (RED) showing that any inverse problem …
Total variable-order variation as a regularizer applied on multi-frame image super-resolution
Multi-frame image super-resolution reconstruction focuses on obtaining a high-resolution
(HR) image from a low-resolution image. Since the super-resolution problem is ill-posed, it is …
(HR) image from a low-resolution image. Since the super-resolution problem is ill-posed, it is …
An improved bilevel optimization approach for image super-resolution based on a fractional diffusion tensor
Variational regularization techniques are widely used to improve the quality of the super-
resolved image. However, the success of these methods depends on some sensitive …
resolved image. However, the success of these methods depends on some sensitive …
Satellite video super-resolution based on adaptively spatiotemporal neighbors and nonlocal similarity regularization
Recently, super-resolution (SR) of satellite videos has received increasing attention as it can
overcome the limitation of spatial resolution in applications of satellite videos to dynamic …
overcome the limitation of spatial resolution in applications of satellite videos to dynamic …
Robust multi-frame super-resolution based on spatially weighted half-quadratic estimation and adaptive BTV regularization
Multi-frame image super-resolution focuses on reconstructing a high-resolution image from
a set of low-resolution images with high similarity. Combining image prior knowledge with …
a set of low-resolution images with high similarity. Combining image prior knowledge with …