지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.8
- 수록면
- 220 - 232 (13page)
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초록· 키워드
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Pseudo-deconvolution is an effective approach to restore space variant degradation (SVD), where each sub-problem is treated as an optimization problem that simultaneously performs image de-blurring and de-noising. De-blurring is an inverse problem primary to visual processing systems. It becomes ill-posed if noise taints the blurry image. Thus the problem is very sensitive to small perturbation in the data. Additive noise has made obsolete classical approaches of inverse filtering and linear algebraic restorations. The restoration formulation for the ill-posed inverse degradations is constrained least square error (CLSE) minimization. Generally, regularization of solutions by “smoothness constraint” is an addition in the classical approaches to cater to the sensitivity of solutions for small perturbations. In this paper, two well-known evolutionary computation (EC) algorithms: 1) the genetic algorithm (GA) with binary and real encoding schemes, and 2) the particle swarm algorithm (PSO), are proposed to evolve the estimated image in order to obtain an optimal solution for restoration with adaptive regularization. Thus the restoration framework presented in this paper is new and novel, such that it reconstructs the image guided by evolutionary computations. Furthermore, modifications in the initial evolutionary framework are proposed to make it a novel hybrid meta-heuristic approach for real-world restoration applications. Quantitative and visual results of the proposed framework are presented in the paper, with comparative analysis within the EC domain and state-of-the-art methods.
목차
Abstract
Introduction
Related Work
Proposed Evolutionary Reconstruction for Image Restoration
Experimental Results and Discussion
Conclusions
References
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UCI(KEPA) : I410-ECN-0101-2015-500-002466524