Hybrid Recursive Super-resolution Image Reconstruction using Neural Networks

2007 ◽  
Author(s):  
Di Zhang ◽  
Xuelan Miao ◽  
Jiazhong He
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Resolution Image

scholarly journals Convolutional Neural Networks Using Skip Connections with Layer Groups for Super-Resolution Image Reconstruction Based on Deep Learning

2020 ◽  
Vol 10 (6) ◽  
pp. 1959
Author(s):  
Hyeongyeom Ahn ◽  
Changhoon Yim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Resolution Image ◽  
Hidden Layer ◽  
Layer Groups

In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.

Fast MAP-based multiframe super-resolution image reconstruction

2005 ◽  
Vol 23 (7) ◽  
pp. 671-679 ◽  
Author(s):  
Di Zhang ◽  
Huifang Li ◽  
Minghui Du
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Resolution Image

A soft MAP framework for blind super-resolution image reconstruction

2009 ◽  
Vol 27 (4) ◽  
pp. 364-373 ◽  
Author(s):  
Yu He ◽  
Kim-Hui Yap ◽  
Li Chen ◽  
Lap-Pui Chau
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Resolution Image
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