Images of di®erent origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classi¯cation, 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¯ltering behavior before any denoising is applied. This paper studies the e±ciency of texture image denoising for di®erent noise intensities and several¯lter types under di®erent visual quality criteria (quality metrics). It is demonstrated that the most e±cient existing¯lters 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
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.
This paper deals with one way to predict efficiency of despeckling for images acquired by a single-or multi-look synthetic aperture radar (SAR). A particular filter based on discrete cosine transform (DCT) and adapted to speckle characteristics is considered. Two quantitative criteria characterizing despeckling efficiency are analyzed. It is shown that even one parameter that can be calculated for image blocks sequentially or in parallel allows carrying out a rather accurate prediction. Moreover, such a prediction is possible for both spatially uncorrelated and correlated speckle.
Radar imaging has many advantages. Meanwhile, SAR images suffer from a noise-like phenomenon called speckle. Many despeckling methods have been proposed to date but there is still no common opinion as to what the best filter is and/or what are its parameters (window or block size, thresholds, etc.). The local statistic Lee filter is one of the most popular and best-known despeckling techniques in radar image processing. Using this filter and Sentinel-1 images as a case study, we show how filter parameters, namely scanning window size, can be selected for a given image based on filter efficiency prediction. Such a prediction can be carried out using a set of input parameters that can be easily and quickly calculated and employing a trained neural network that allows determining one or several criteria of filtering efficiency with high accuracy. The statistical analysis of the obtained results is carried out. This characterizes improvements due to the adaptive selection of the filter window size, both potential and based on prediction. We also analyzed what happens if, due to prediction errors, erroneous decisions are undertaken. Examples for simulated and real-life images are presented.
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
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