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C. DUMITRESCU1,*
Most images have non-stationary properties, so they contain smooth regions and regions with abrupt transitions. These regions with different characteristics can be well highlighted in the wavelet domain. The wavelet decomposition of an image contains high and low energy areas (or coefficients with high and low absolute values). Maintaining the idea of point-wise thresholding and exploiting the interband dependence by determining a map of accounts using the concept of hierarchical correlation, in this article we propose and develop a new spatial adaptive filter that allows point-wise thresholding and exploits the interband dependence by determining a map of accounts using the concept of hierarchical correlation, being more efficient in terms of computational effort than the adaptive spatial filtering of wavelet coefficients with contextual modeling. The proposed algorithm was tested on different images, and from the analysis of the obtained data it appears that the effect of the threshold value used to obtain the contour map from the hierarchical correlation map does not depend on the processed image (as in the case of optimal threshold values in the case of soft truncation), but only on the dispersion of the disturbing noise.
Wavelet, Adaptive filter, Noise reduction, Image processing.
C. DUMITRESCU, Spatially adaptive soft truncation based on the hierarchical correlation map and with the establishment of the threshold value based on local statistics, Optoelectronics and Advanced Materials - Rapid Communications, 19, 7-8, July-August 2025, pp.384-391 (2025).
Submitted at: April 4, 2025
Accepted at: Aug. 4, 2025
Optoelectronics and Advanced Materials - Rapid Communications
(2024): Impact Factor = 0.4 - Journal Citation Reports (Clarivate Analytics, 2025)
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