tailieunhanh - Báo cáo hóa học: " A Nonlinear Entropic Variational Model for Image Filtering"

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 Nonlinear Entropic Variational Model for Image Filtering | EURASIP Journal on Applied Signal Processing 2004 16 2408-2422 2004 Hindawi Publishing Corporation A Nonlinear Entropic Variational Model for Image Filtering A. Ben Hamza Concordia Institute for Information Systems Engineering Concordia University Montreal Quebec H3G 1T7 Canada Email hamza@ Hamid Krim Department of Electrical and Computer Engineering North Carolina State University Raleigh NC 27695-7911 USA Email ahk@ JosianeZerubia Ariana Research Group INRIA I3S BP 93 06902 Sophia Antipolis Cedex France Em ail bia@ Received 12 August 2003 Revised 8 June 2004 We propose an information-theoretic variational filter for image denoising. It is a result of minimizing a functional subject to some noise constraints and takes a hybrid form of a negentropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping to construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrative experimental results demonstrate a much improved performance of the approach in the presence of Gaussian and heavy-tailed noise. Keywords and phrases MAP estimation variational methods robust statistics differential entropy gradient descent flows image denoising. 1. INTRODUCTION In recent years variational methods and partial differential equations- PDE based methods 1 2 3 4 5 6 have been introduced to explicitly account for intrinsic geometry to address a variety of problems including image segmentation mathematical morphology motion estimation image classification and image denoising 7 8 9 10 11 12 . The latter will be the focus of the present paper. The problem of sig-nal image denoising has been addressed using a number of different techniques including wavelets 13 order statisticsbased filters 14 PDE-based algorithms 9 15 and variational approaches 16 17 .