Efficiency of texture image enhancement by DCT-based filtering

Abstract

International audienceTextures or high-detailed structures as well as image object shapes contain information that is widely exploited in pattern recognition and image classification. Noise can deteriorate these features and has to be removed. In this paper, we consider the influence of textural properties on efficiency of image enhancement by noise suppression for the posterior treatment. Among possible variants of denoising, filters based on discrete cosine transform known to be effective in removing additive white Gaussian noise are considered. It is shown that noise removal in texture images using the considered techniques can distort fine texture details. To detect such situations and to avoid texture degradation due to filtering, filtering efficiency predictors, including neural network based predictor, applicable to a wide class of images are proposed. These predictors use simple statistical parameters to estimate performance of the considered filters. Image enhancement is analysed in terms of both standard criteria and metrics of image visual quality for various scenarios of texture roughness and noise characteristics. The discrete cosine transform based filters are compared to several counterparts. Problems of noise removal in texture images are demonstrated for all of them. A special case of spatially correlated noise is considered as well. Potential efficiency of filtering is analysed for both studied noise models. It is shown that studied filters are close to the potential limits

Is Texture Denoising Efficiency Predictable?

“…Therefore, a task is to remove this noise preserving the texture features in a maximally careful manner. 17,32,35,36,46,54 One might expect that this task of e±cient texture denoising which was already relevant a decade or two ago, 17,32,46 and now with all recent advancements (nonlocal ltering methods) in image denoising 11,12,14,26,46 has been successfully solved. However, this is not true.…”

“…The paper in Ref. 36 does not present data for the¯lters GHP, LPG-PCA, SAIF, KSVD, and KLLD. 3,6,43,50,54 The paper in Ref.…”

“…Firstly, visual quality of denoised images is important for many applications. 23,36,49 Thus, it is expedient to employ adequate visual quality metrics 23,28,41 in the analysis of¯ltering e±ciency. Secondly, a positive e®ect from denoising (noise suppression) is often comparable to a negative e®ect of texture smearing or distorting.…”

“…Secondly, a positive e®ect from denoising (noise suppression) is often comparable to a negative e®ect of texture smearing or distorting. 36 Then, the following question arises -is it worth applying denoising at all? Accompanying questions are: can we predict expedience of ltering for each particular case, and is it possible to reliably undertake a decision to carry out or to skip denoising?…”

“…1,35,36,34 showing that the accuracy depends upon many factors including what are the input and output parameters, how regression is calculated, what¯lter is analyzed, etc. Good prediction characteristics are already obtained for many modern¯lters if IPSNR is predicted.…”