Blind Deconvolution Method using Omnidirectional Gabor Filter-based Edge Information

TL;DR

This paper introduces a blind deconvolution method that leverages omnidirectional Gabor filter-based edge information to improve image sharpness, utilizing multiple edge directions and a Haar defocus score for quality assessment.

Contribution

It proposes a novel edge-based blind deconvolution technique using omnidirectional Gabor filters and introduces a Haar defocus score for better image quality measurement.

Findings

Achieves higher deblurring quality as measured by Haar defocus score.

Outperforms existing methods in Peak Signal to Noise Ratio.

Utilizes multiple edge directions for improved image recovery.

Abstract

In the previous blind deconvolution methods, de-blurred images can be obtained by using the edge or pixel information. However, the existing edge-based methods did not take advantage of edge information in ommi-directions, but only used horizontal and vertical edges when recovering the de-blurred images. This limitation lowers the quality of the recovered images. This paper proposes a method which utilizes edges in different directions to recover the true sharp image. We also provide a statistical table score to show how many directions are enough to recover a high quality true sharp image. In order to grade the quality of the deblurring image, we introduce a measurement, namely Haar defocus score that takes advantage of the Haar-Wavelet transform. The experimental results prove that the proposed method obtains a high quality deblurred image with respect to both the Haar defocus score and the Peak Signal to Noise Ratio.