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: A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets | EURASIP Journal on Applied Signal Processing 2005 8 1205-1211 2005 Hindawi Publishing Corporation A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets Erdem Bala Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA Email erdem@ Aysin Ertuzun Electrical and Electronics Engineering Department Bogazici University 34342 Bebek Istanbul Turkey Email ertuz@ Received 20 January 2004 Revised 21 November 2004 Recommended for Publication by Kiyoharu Aizawa Multiwavelets wavelets with several scaling functions offer simultaneous orthogonality symmetry and short support which is not possible with ordinary scalar wavelets. These properties make multiwavelets promising for signal processing applications such as image denoising. The common approach for image denoising is to get the multiwavelet decomposition of a noisy image and apply a common threshold to each coefficient separately. This approach does not generally give sufficient performance. In this paper we propose a multivariate thresholding technique for image denoising with multiwavelets. The proposed technique is based on the idea of restoring the spatial dependence of the pixels of the noisy image that has undergone a multiwavelet decomposition. Coefficients with high correlation are regarded as elements of a vector and are subject to a common thresholding operation. Simulations with several multiwavelets illustrate that the proposed technique results in a better performance. Keywords and phrases multiwavelets image denoising multivariate thresholding. 1. INTRODUCTION Multiwavelets are a relatively new addition to the wavelet theory and have received considerable attention since their introduction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 . Contrary to ordinary wavelets multiwavelets offer simultaneous orthogonality symmetry and short support. Similar to performing wavelet decomposition with filters multiwavelet decomposition can be realized with
