Efficiency of DCT-Based Denoising Techniques Applied to Texture Images

Abstract

Textures or high-detailed structures contain information that can be exploited in pattern recognition and classification. If an acquired image is noisy, noise removal becomes an operation to improve image quality before further stages of processing. Among possible variants of denoising, we consider filters based on orthogonal transforms, in particular, on discrete cosine transform (DCT) known to be able to effectively remove additive white Gaussian noise (AWGN). Besides, we study a representative of nonlocal denoising techniques, namely, BM3D known as state-of-the-art technique based on DCT and similar patch search. We show that noise removal in texture images using the considered DCT-based techniques can distort fine texture details. To detect such situations and avoid texture degradation due to filtering, we propose to apply filtering efficiency prediction tests applicable to wide class of images. These tests are based on DCT coefficient statistic parameters and can be used for decision-making in relation to the use of the considered filters.

Prediction of Despeckling Efficiency of DCT-Based Filters Applied to SAR Images

“…Meanwhile, there are recently proposed approaches to prediction [15,[18][19][20][21] that are fast and based on another principle. It is supposed that there exists a rather strict dependence between one or several "input" parameters (that can be easily calculated for an analyzed image) and a parameter that characterizes denoising efficiency.…”

“…Recently, some steps towards designing prediction procedures have been done [15,[17][18][19][20][21]. In [17], it has been shown that for non-local denoising methods applied to remove additive white Gaussian noise (AWGN) it is possible to predict potential output mean square error (MSE) without having the corresponding reference image.…”

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.…”

“…27 It has been shown in Refs. 35 and 36 that the problems in noise removal arise for¯lters based on discrete cosine transform (DCT) 16,22,26 and one of the most advanced nonlocal¯l-tering methods, BM3D (block matching three dimensional)¯lter. 11 Then, one might think that denoising techniques based on other principles are able to cope with a noise in texture images in a better way.…”

“…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.…”