0524621 20240103224111.420200601235959.9 000510098800001 85078770837 10.1007/s11045-020-00702-7 30 s. P cav_un_epca*02572860923-6082313 (2020)1113-1143Springer Multivariate density Gaussian additive noise Noise-robust estimation Moments Invariant characteristics cav_un_auth*0336802 Kostková Jitka UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/ZOI/flusser-0524621.pdf https://link.springer.com/article/10.1007/s11045-020-00702-7 cav_un_auth*0360229 GA18-07247S GA ČR cav_un_auth*0392630 SG18/188/OHK4/3T/14 GA CVUT CZ Observation of random variables is often corrupted by additive Gaussian noise. Noisereducing data processing is time-consuming and may introduce unwanted artifacts. In this paper, a novel approach to description of random variables insensitive with respect to Gaussian noise is presented. The proposed quantities represent the probability density function of the variable to be observed, while noise estimation, deconvolution or denoising are avoided. Projection operators are constructed, that divide the probability density function into a non-Gaussian and a Gaussian part. The Gaussian part is subsequently removed by modifying the characteristic function to ensure the invariance. The descriptors are based on the moments of the probability density function of the noisy random variable. The invariance property and the performance of the proposed method are demonstrated on real image data. WOS JD 20000 20200 20204 2021 2 4 A sml 4as 20231122144933.0 RVO:67985556 http://hdl.handle.net/11104/0308967 S Copyright 20200122 3+4 Article Computer Science Theory Methods|Engineering Electrical Electronic C COMPUTERSCIENCE.THEORY&METHODS|ENGINEERING.ELECTRICAL&ELECTRONIC 1.920 0.624 3.5 0.00163 0.41 1125 48 0.337 36.39 2.13 622 Q3 1.904 101 Q2 33.494 51.3 Q3 Q3 0.64 Q2 63.182 2020 Soubory v repozitáři: flusser-kostkova-0524621-copyright.pdf 85078770837 SCOPUS PUBMED 000510098800001 WOS cav_un_epca*0257286 Multidimensional Systems and Signal Processing 0923-6082 1573-0824 Roč. 31 č. 3 2020 1113 1143 Springer