Abstract
In order to improve the quality of restored image, a blind image restoration algorithm is proposed, in which both the Signal-to-Noise Ratio (SNR) and the Gaussian Point Spread Function (PSF) of the degraded image are estimated. Firstly, the SNR of the degraded image is estimated through local deviation method. Secondly, the PSF of the degraded image is estimated through error-parameter method. Thirdly, Utilizing the estimated SNR and PSF, high resolution image is restored through Wiener filtering restoration algorithm. Experimental results show that the quality and peak signal-to-noise of the restored image are better around the real value and justify the fact that the SNR an-d PSF estimation plays great important part in blind image restoration.
Highlights
High resolution images are often required in many areas such as medical imaging, military, remote sensing, etc
The Point Spread Function (PSF) and Signal-to-Noise Ratio (SNR) are estimated and their importance in the quality of restored image is justified through experimental results
The experimental results show that the quality of the restored images is better around the real SNR and PSF
Summary
High resolution images are often required in many areas such as medical imaging, military, remote sensing, etc. The PSF and SNR are estimated and their importance in the quality of restored image is justified through experimental results.
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