Non-blind image deconvolution using sampling without replacement

Jaeduk Han, Jonghyun Kim, Moon Gi Kang

Research output: Contribution to journalConference articlepeer-review


Image degradations can be modeled as a process of linear systems which are usually denoted by convolution. Deconvolution refers to a reverse operation of the linear system in which an original image is convolved with a blur kernel, which is also known as a point spread function (PSF) of the linear system. If the blur kernel, which can represent linear degradations such as an out-of-focus blur or motion blurs due to the shake of a camera, is known, we call this ill-posed problem the non-blind deconvolution problem. In this paper, we propose a non-blind deconvolution method using a convex optimization method in which a non-derivative approach is used to solve the ill-posed problem. The proposed method minimizes the objective function using the stochastic process in which the random variable selects the coordinate. The objective function is minimized along the selected coordinate direction at each iteration. If several coordinate directions are picked simultaneously, the cost can be decreased independently along each coordinate direction.

Original languageEnglish
Article numbers19
Pages (from-to)125-130
Number of pages6
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Publication statusPublished - 2017
EventImage Processing: Algorithms and Systems XV, IPAS 2017 - Burlingame, United States
Duration: 2017 Jan 292017 Feb 2

Bibliographical note

Publisher Copyright:
© 2017, Society for Imaging Science and Technology.

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


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