A Learning Based Single Image Super Resolution Method Using Directionlets

2010 ◽  
Author(s):  
Reji A.P. ◽  
Tessamma Thomas
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

2020 ◽  
Vol 22 (6) ◽  
pp. 1407-1422
Author(s):  
Yunfeng Zhang ◽  
Ping Wang ◽  
Fangxun Bao ◽  
Xunxiang Yao ◽  
Caiming Zhang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Iterative Approximation ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A fast single-image super-resolution method implemented with CUDA

2018 ◽  
Vol 16 (1) ◽  
pp. 81-97 ◽  
Author(s):  
Yuan Yuan ◽  
Xiaomin Yang ◽  
Wei Wu ◽  
Hu Li ◽  
Yiguang Liu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3351
Author(s):  
Yooho Lee ◽  
Dongsan Jun ◽  
Byung-Gyu Kim ◽  
Hunjoo Lee
Keyword(s):  
Super Resolution ◽  
Perceptual Quality ◽  
Dense Network ◽  
Single Image ◽  
Resolution Method ◽  
Multi Scale ◽  
Training Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sr Method

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.

scholarly journals Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

2021 ◽  
Vol 6 (1) ◽  
pp. 1-6
Author(s):  
Ayush Singh ◽  
Mehran Ebrahimi
Keyword(s):  
Video Sequence ◽  
Structural Information ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Edge Information ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.

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