ANALYSIS OF OPPORTUNITIES TO IMPROVE IMAGE DENOISING EFFICIENCY FOR DCT-BASED FILTER

• Main Authors: Oleksandr Nikolaevich Zemliachenko, Inna Grigoryevna Ivakhnenko, Galina Anatolyevna Chernova, Vladimir Vasilyevich Lukin
• Format: Article
• Language: English
• Published: National Aerospace University «Kharkiv Aviation Institute» 2018-10-01
• Series: Радіоелектронні і комп'ютерні системи
• Subjects: image denoising; dct-based filter; parameter optimization; performance criteria
• Online Access: http://nti.khai.edu/ojs/index.php/reks/article/view/46

The subject matter of the paper is the process of image filtering. The goal is to provide high efficiency of denoising according to metrics that are more adequate to human vision system than traditional criteria as mean square error or peak signal-to-noise ratio. The tasks to be solved are the following: to carry out analysis of denoising efficiency using visual quality metric, to determine optimal settings of DCT-based filter depending upon image and noise properties, to propose a method for setting a global threshold adaptively (in quasi-optimal manner) based on preliminary analysis of image and noise properties. The following results have been obtained: 1) optimal values of filter parameters depend upon many factors including image complexity and noise intensity, 2) optimal values also depend on optimization criterion (or metric) used to characterize filter performance; 3) optimal values of parameter β that determines the threshold can considerably differ from 2.6 which is traditionally recommended; 4) this opens opportunities for improving image denoising efficiency; 5) one of this opportunities consists in preliminary analysis of image and noise properties with setting the threshold value according to the obtained dependences. Conclusions: 1) the filter performance can be sufficiently improved due to the proposed adaptive procedure; 2) this happens if either noise is intensive and image has a simple structure or if noise is not too intensive and image has a complex structure; 3) the proposed adaptive procedure requires a very small amount of additional computations for calculating input parameter and can be realized by 60 or more times faster than filtering itself; 4) the adaptive procedure slightly differs depending upon a metric used as performance criterion.