REMOVAL OF ARTIFACTS IN MEDICAL IMAGES
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Date
2019-06-13T10:36:04Z
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Abstract
Images are produced to record or display useful information or details .Due to flaws in the imaging and capturing process, however, the recorded image always represents a degraded version of the original scene. The undoing of these imperfections is critical to many of the successive image processing tasks .There exists a huge range of different degradations, which should be taken into account, for example noise, geometrical degradations, illumination and color imperfections (under-exposure/over-exposure, saturation) and blur. The area of image restoration (sometimes referred to as image deblurring or image deconvolution) is concerned with the reconstruction or estimation of the uncorrupted image from a blurred and noisy image. Essentially, it tries to perform an operation on the image which is the inverse of the imperfections in the image formation system. In the use of image restoration methods, the characteristics of the degrading system and the noise are assumed to be known. The aim of this work is to provide a concise overview of most useful restoration models .Different types of image restoration techniques like Wiener filter, Inverse filter, Regularized filter and Richardson–Lucy algorithm are described and strength and weakness of each approach are identified. In this project we have designed different filters namely Weiner, gaussian, median , regularised,inverse , Lucy Richardson, and adaptive Gaussian the noise is removed by applying the filtering methods mentioned above.
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1NH15EC002, 1NH15EC058