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

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

Is Texture Denoising Efficiency Predictable?

Images of different origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classification, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the filtering behavior before any denoising is applied. This paper studies the efficiency of texture image denoising for different noise intensities and several filter types under different visual quality criteria (quality metrics). It is demonstrated that the most efficient existing filters provide very similar results. From the obtained results, it is possible to generalize and employ the prediction strategy earlier proposed for denoising techniques based on the discrete cosine transform. Accuracy of such a prediction is studied and the ways to improve it are considered. Some practical recommendations concerning a decision to undertake whether it is worth applying a filter are given.publishedVersionPeer reviewe