Super-Resolution Method by Registration of Multi-scale Components Considering Color Sampling Pattern and Frequency Spectrum Power of UHDTV Camera

Author(s):  
Yasutaka Matsuo ◽  
Shinichi Sakaida
2015 ◽  
Vol 29 (12) ◽  
pp. 2095-2120 ◽  
Author(s):  
Linwei Yue ◽  
Huanfeng Shen ◽  
Qiangqiang Yuan ◽  
Liangpei Zhang

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3351
Author(s):  
Yooho Lee ◽  
Dongsan Jun ◽  
Byung-Gyu Kim ◽  
Hunjoo Lee

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.


2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Mehrdad Sheoiby ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars Petersson

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