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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 …
Image super-resolution by neural texture transfer
Due to the significant information loss in low-resolution (LR) images, it has become
extremely challenging to further advance the state-of-the-art of single image super …
extremely challenging to further advance the state-of-the-art of single image super …
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Recently, several models based on deep neural networks have achieved great success in
terms of both reconstruction accuracy and computational performance for single image …
terms of both reconstruction accuracy and computational performance for single image …
Single image super-resolution from transformed self-exemplars
Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing
results without extensive training on external databases. Such algorithms exploit the …
results without extensive training on external databases. Such algorithms exploit the …
Image restoration using convolutional auto-encoders with symmetric skip connections
Image restoration, including image denoising, super resolution, inpainting, and so on, is a
well-studied problem in computer vision and image processing, as well as a test bed for low …
well-studied problem in computer vision and image processing, as well as a test bed for low …
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to improve example-
based single image super resolution (SR): 1) augmentation of data, 2) use of large …
based single image super resolution (SR): 1) augmentation of data, 2) use of large …
Convolutional sparse coding for image super-resolution
Sparse coding (SC) plays an important role in versatile computer vision applications such as
image super-resolution (SR). Most of the previous SC based SR methods partition the image …
image super-resolution (SR). Most of the previous SC based SR methods partition the image …
Coupled deep autoencoder for single image super-resolution
Sparse coding has been widely applied to learning-based single image super-resolution
(SR) and has obtained promising performance by jointly learning effective representations …
(SR) and has obtained promising performance by jointly learning effective representations …
A fast medical image super resolution method based on deep learning network
S Zhang, G Liang, S Pan, L Zheng - IEEE Access, 2018 - ieeexplore.ieee.org
Low-resolution medical images can hamper medical diagnosis seriously, especially in the
analysis of retina images and specifically for the detection of macula fovea. Therefore …
analysis of retina images and specifically for the detection of macula fovea. Therefore …
Underwater image super-resolution by descattering and fusion
Underwater images are degraded due to scatters and absorption, resulting in low contrast
and color distortion. In this paper, a novel self-similarity-based method for descattering and …
and color distortion. In this paper, a novel self-similarity-based method for descattering and …