Image Deblurring with Compressive Sensing
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Date
2021-12
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Addis Ababa University
Abstract
Compressive sensing is a technique which enables recovery of signals that are represented by an underdetermined system of equations. Such a recovery of an original signal is made possible if the samples are represented in a sparse manner provided an appropriate measuring matrix is used for the modeled system. Blurred images are examples of signals that are sparse especially in transform domains. Different researches have been done to show the possibility of recovering blurred images that use sparse representation of transform domains by applying compressive sensing. In this thesis, however, a model has been used that doesn’t require transforming into other domains. In addition, a box-wise approach has been used that derives the underdetermined system matrix from 7x7 segmented boxes of the blurred image. Then compressive sensing algorithms are used to recover the whole image iteratively. This method is shown to have a much better computational complexity, for example, than the traditional Lucy-Richardson deblurring but it has limitations due to approximations used in the 7x7 boxes during modeling. Thus, with this improved computational complexity, the study provides an initial platform to deblur images using box-wise method and compressive sensing theories.
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Keywords
Image Deblurring, Sensing, Compressive sensing