Efficiency of texture image enhancement by DCT-based filtering

Aleksey Rubel (1) , Vladimir Lukin (1) , Mikhail Uss (1) , Benoit Vozel (2, 3) , Oleksiy Pogrebnyak (4) , Karen Egiazarian (5)

1 Kharkov National University
2 ENSSAT - École Nationale Supérieure des Sciences Appliquées et de Technologie
3 IETR - Institut d'Électronique et des Technologies du numéRique
4 CIC-IPN - Centro de Investigación en Computación
5 Department of Signal Processing [Tampere]

Textures 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.

Keywords: DCT-based filtering; image denoising; Texture analysis; Visual quality

Domains: Engineering Sciences [physics]; Electronics; Computer Science [cs]; Networking and Internet Architecture [cs.NI]

Aleksey Rubel, Vladimir Lukin, Mikhail Uss, Benoit Vozel, Oleksiy Pogrebnyak, et al.. Efficiency of texture image enhancement by DCT-based filtering. Neurocomputing, 2016, 175 Part B, pp.948-965. ⟨10.1016/j.neucom.2015.04.119⟩. ⟨hal-01240773⟩