Lightweight image super-resolution based on deep learning: State-of-the-art and future directions
Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …
more and more attention, aiming to improve the images and videos resolutions. Most of the …
Deep learning for image super-resolution: A survey
Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …
enhance the resolution of images and videos in computer vision. Recent years have …
[PDF][PDF] A comprehensive review of deep learning-based single image super-resolution
SMA Bashir, Y Wang, M Khan, Y Niu - PeerJ Computer Science, 2021 - peerj.com
Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …
resolution of an image in the field of computer vision. In the last two decades, significant …
Practical single-image super-resolution using look-up table
A number of super-resolution (SR) algorithms from in terpolation to deep neural networks
(DNN) have emerged to restore or create missing details of the input low-resolution image …
(DNN) have emerged to restore or create missing details of the input low-resolution image …
Enhanced deep residual networks for single image super-resolution
Recent research on super-resolution has progressed with the development of deep
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …
Lightweight image super-resolution with enhanced CNN
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved
impressive performances on single image super-resolution (SISR). However, their excessive …
impressive performances on single image super-resolution (SISR). However, their excessive …
Image super-resolution via attention based back projection networks
Deep learning based image Super-Resolution (SR) has shown rapid development due to its
ability of big data digestion. Generally, deeper and wider networks can extract richer feature …
ability of big data digestion. Generally, deeper and wider networks can extract richer feature …
LKASR: Large kernel attention for lightweight image super-resolution
H Feng, L Wang, Y Li, A Du - Knowledge-Based Systems, 2022 - Elsevier
Image super-resolution, aims to recover a corresponding high-resolution image from a given
low-resolution image. While most state-of-the-art methods only consider using fixed small …
low-resolution image. While most state-of-the-art methods only consider using fixed small …
Feedback network for image super-resolution
Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …
achieve a better reconstruction performance. However, the feedback mechanism, which …
Image super resolution by dilated dense progressive network
P Shamsolmoali, J Zhang, J Yang - Image and vision computing, 2019 - Elsevier
Image super-resolution (SR) is an interesting topic in computer vision. However, it remains
challenging to achieve high-resolution image from the corresponding low-resolution version …
challenging to achieve high-resolution image from the corresponding low-resolution version …