Image Blur Removal By Adaptive Filtering
Volume 3 - Issue 4, April 2019 Edition
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Author(s)
Abdussalam Alhadi Addeeb, Ahmed Abdullah Aburas
Keywords
Adaptive filter, Performance measures, and Motion & Gaussian blur.
Abstract
Digital image processing (DIP) has many significant advantages over analog image processing. DIP allows a much wider range of algorithms to treat the impairment effects such as the build-up of blurred and noisy signal distortion during the diverse image processes. This paper is to assess couple of adaptive filters in estimating an image out of blur distortion. The image is distorted with Motion blur and Gaussian blur separately. The utilized restoration techniques are Lucy Richardson (LR) Algorithm filter and Wiener filter (WF). The proposed filters are tested against the color image. Different performance measures are employed, including, Mean Square Error (MSE), Performance Index (PI), and Peak Signal-to-Noise Ratio (PSNR). Repeatedly, these measures show that LR filter demonstrates a much higher performance than its counterpart. In fact, this filter outcome shows a decent match between subjective and objective assessments of the images.
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