본 연구실 졸업생인 이해근 박사가 연구한 “Overlapping Group Prior for Image Deconvolution Using Patch-Wise Gradient Statistics” 제목의 논문이 SIGNAL PROCESSING (Impact factor : 4.729, Rank : Q2)에 게재가 확정되었다. 본 연구는 이해근 박사(1저자)와 김한솔 학생(2저자)과 이진욱 학생(2저자), 강문기 교수(교신저자)가 진행하였다.

Abstract

In image deconvolution problems, global gradient statistics serve as powerful image priors that separate high-quality images from degraded observations. However, it is unclear whether the important detailed information is sufficiently regularized in these approaches. Owing to these limitations, the restored images exhibit sharply reconstructed edges around structures, but commonly suffer from the loss of significant textures and details. In this study, we introduced patch-wise gradient statistics as an image prior strategy to restore disregarded regions in consideration of regional characteristics. The analytic relation between the prior and the restored local statistics was investigated through the Bayesian interpretation of overlapping group sparsity. The regularization function that incorporates the contextual information was derived in the maximum a posteriori framework. Subsequently, the unknown prior information and latent image were alternately estimated from the observations based on the patch-wise statistical analysis of the non-blind deconvolution process. In the experimental results, the superiority of the proposed algorithm was verified by more original textures in the restored images compared to the conventional approaches.