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: Research Article Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 42472 10 pages doi 2007 42472 Research Article Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal Rusen Oktem 1 Karen Egiazarian 2 Vladimir V. Lukin 3 Nikolay N. Ponomarenko 3 and Oleg V. Tsymbal4 1 Electrical and Electronics Engineering Department Atilim University Kizilcasar Koyu 06836 Incek Ankara Turkey 2 Institute of Signal Processing Tampere University of Technology 33101 Tampere Finland 3 Department of Receivers Transmitters and Signal Processing National Aerospace University 17 Chkalova Street 61070 Kharkov Ukraine 4 Kalmykov Center for Radiophysical Sensing of Earth 12 Ak. Proskury Street 61085 Kharkov Ukraine Received 13 October 2006 Revised 21 March 2007 Accepted 13 May 2007 Recommended by Stephen Marshall This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally that is within a window of small support DCT is expected to approximate the Karhunen-Loeve decorrelating transform which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases when signaldependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform is formulated. Although the main issue is signal-dependent and pure multiplicative noise the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images. Copyright 2007 Rusen Oktem et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction .
