Robust single image super resolution using neighbor embedding and fusion in wavelet domain

This paper proposes methods for super resolving single noisy low resolution images. Even if single image super resolution has been a topic of research for last few decades, super resolution of noisy low resolution images is still a challenging problem. Most of the state of the art super resolution a...

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Bibliographic Details
Published in:Computers & electrical engineering 2018-08, Vol.70, p.674-689
Main Authors: Rahiman V, Abdu, George, Sudhish N
Format: Article
Language:English
Subjects:
Algorithms
Denoising
Image enhancement
Image processing systems
Image resolution
Noise reduction
Signal to noise ratio
Sparse representation
State of the art
Super resolution
Wavelet analysis
Wavelet fusion
Wavelet transforms
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Summary:This paper proposes methods for super resolving single noisy low resolution images. Even if single image super resolution has been a topic of research for last few decades, super resolution of noisy low resolution images is still a challenging problem. Most of the state of the art super resolution algorithms will fail to perform if significant amount of noise is present in the observed image. In this paper, we propose a denoised patch dictionary based single image super resolution algorithm. To enhance the robustness to noise performance, this method is further modified by using a wavelet based fusion algorithm which combines the result of proposed method with direct super resolved image, and super resolved image after denoising to preserve the finer details of the super resolved image. The proposed methods are applied on the commonly used test images. The results validate that the proposed methods show improvement over the existing techniques.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2017.09.020