Gradient-based image deconvolution

Heyan Huang; Hang Yang; Siliang Ma

doi:10.1117/1.JEI.22.1.013006

7 January 2013 Gradient-based image deconvolution

Heyan Huang, Hang Yang, Siliang Ma

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 1, 013006 (January 2013). https://doi.org/10.1117/1.JEI.22.1.013006

Abstract

Image restoration and deconvolution from blurry and noisy observation is known to be ill-posed. To stabilize the recovery, total variation (TV) regularization is often utilized for its beneficial edge in preserving the image’s property. We take a different approach of TV regularization for image restoration. We first recover horizontal and vertical differences of images individually through some successful deconvolution algorithms. We restore horizontal and vertical difference images separately so that each is more sparse or compressible than the corresponding original image with a TV measure. Then we develop a novel deconvolution method that recovers the horizontal and vertical gradients, respectively, and then estimate the original image from these gradients. Various experiments that compare the effectiveness of the proposed method against the traditional TV methods are presented. Experimental results are provided to show the improved performance of our method for deconvolution problems.

Citation Download Citation

Heyan Huang, Hang Yang, and Siliang Ma "Gradient-based image deconvolution," Journal of Electronic Imaging 22(1), 013006 (7 January 2013). https://doi.org/10.1117/1.JEI.22.1.013006

Published: 7 January 2013

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