UPDCNN: A NEW SCHEME FOR IMAGE UPSAMPLING AND DEBLURRING USING A DEEP CONVOLUTIONAL NEURAL NETWORK

被引：0
|
作者：
Esmaeilzehi, Alireza [1 ]
Ahmad, M. Omair [1 ]
Swamy, M. N. S. [1 ]
机构：
[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ, Canada
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2019年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Image Upsampling and Deblurring; Image Restoration; Deep Learning; SUPERRESOLUTION;
D O I：
10.1109/icip.2019.8803167
中图分类号：
TB8 [摄影技术];
学科分类号：
0804 ;
摘要：
Restoration of a blurred and subsampled image is an illposed problem. In this paper, a two-stage convolutional network is proposed to carry out the processes of upsampling and deblurring to restore the original image. The main idea in the proposed scheme is that the deblurring process is attempted on a high PSNR image obtained after removing the ringing effect that is necessarily caused by the upsampling process. The evaluation of the proposed scheme is carried out using a benchmark dataset in terms of PSNR. The scheme is shown to outperform the state-of-the-art schemes, namely, the sparse coding network, the non-local means filters and the centralized sparse representation.
引用
收藏
页码：2154 / 2158
页数：5
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