Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing | EURASIP Journal on Applied Signal Processing 2005 8 1185-1204 2005 Hindawi Publishing Corporation Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing Oleg V. Tsymbal Kalmykov Center for Radiophysical Sensing of Earth NANU NSAU 61070 Kharkov Ukraine Email dmb@ Vladimir V. Lukin Department of Transmitters Receivers and Signal Processing National Aerospace University 61070 Kharkov Ukraine Email lukin@ Nikolay N. Ponomarenko Department of Transmitters Receivers and Signal Processing National Aerospace University 61070 Kharkov Ukraine Email uagames@ Alexander A. Zelensky Department of Transmitters Receivers and Signal Processing National Aerospace University 61070 Kharkov Ukraine Email zelensky@ Karen O. Egiazarian Institute of Signal Processing Tampere University of Technology 33101 Tampere Finland Email karen@ Jaakko T. Astola Institute of Signal Processing Tampere University of Technology 33101 Tampere Finland Email Received 3 June 2004 Revised 11 October 2004 Recommended for Publication by Moon GiKang Textural features are one of the most important types of useful information contained in images. In practice these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks 1 to analyze the texture preservation properties of various filters and 2 to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For detection of texture regions several classifiers are proposed and analyzed. As shown an
