BLURRING AND DEBLURRING OF DIGITAL IMAGES WITH MATLAB USING GAUSSIAN BLUR AND BLIND DECONVOLUTION ALGORITHMS

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ABSTRACT

This paper is focused on blurring and deblurring of digital images with matlab using gaussian blur and blind deconvolution algorithms. Gaussian blur is the result of blurring an image by a Gaussian function. Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to reveal how Gaussian Blur algorithm for blurring digital images and blind deconvolution algorithm for deblurring digital images can be implemented using MATLAB. Blur is a common problem that occurs when recording digital images due to camera shake, long exposure time, or movement of objects. As a result, the recorded image is degraded and the recorded scene becomes unreadable. There is therefore need to deblur images. The blurring and deblurring of digital images is achieved by implementing point spread function (PSF) in MATLAB and other image processing commands.

1.0 Introduction

A digital image can be considered as a large array of discrete dots, each of which has a brightness associated with it. These dots are called picture elements, or more simply pixels. The pixels surrounding a given pixel constitute its neighbourhood. A neighborhood can be characterized by its shape in the same way as a matrix: we can speak of a 3×3 neighborhood, or of a 5×7 neighborhood. The ability to manipulate pixels using algorithms such as blind deconvolution and Gaussian, makes image processing possible. Reducing the number of pixels in an image for instance, will reduce the quality thereby creating a different effect of the image (Winkler, 2016).

Images are widely used in many kinds of applications such as everyday photography, monitoring, medical imaging, astronomy, microscopy, and remote sensing. Digital images are composed of picture elements or pixels that are organized in a grid. Each pixel contains an intensity value which determines the tone at a specific point. Unfortunately, all captured images end up more or less blurry. The motion of objects or the vibration of the sensor (camera) when pressing the shutter causes the image to be blurred.

Image processing involves changing the nature of an image in order to either improve its pictorial information for human interpretation and render it more suitable for autonomous machine perception. It is necessary to realize that these two aspects represent two separate but equally important aspects of image processing. Humans like their images to be sharp, clear and detailed; machines prefer their images to be simple and uncluttered. For instance, an image can be made blur using  Gaussian blur algorithm and a blurred image can be made to look sharper by removing the blur using blind deconvolution algorithm. Image deblurring methods can be divided into two classes: non-blind, in which the blurring operator is known and blind, in which the blurring operator is unknown (Pulfer, 2019).