Locally adaptive filters are widely used in image processing applications. However, their design commonly requires sufficient efforts and does not take into consideration some important aspects of further processing (interpreting and/or classification) of images. This paper puts forward a novel approach to automatic design of locally adaptive filters subject to further interpretation, namely, detection and localization of small size objects. Design is based on learning with clustering for a test image corrupted by a noise with statistical characteristics observed in real life images to which the obtained filter intend to be further applied. Quantitative data confirming the designed filter efficiency are presented.
The peculiarities of post-processing of multi-look synthetic aperture radar (SAR) images and ultrasound image sequence with the aim of their enhancement are considered. It is shown that it is expedient to apply both several image accumulation and their firther filtering. For test and real data the comparison analysis of several methods of image accumulation and filtering is performed and practical recommendations conceming their application are given.
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