Image denoising by averaging of piecewise constant simulations of image partitions.
This paper investigates the problem of image denoising when the image is corrupted by additive white Gaussian noise. We herein propose a spatial adaptive denoising method which is based on an averaging process performed on a set of Markov Chain Monte-Carlo simulations of region partition maps constrained to be spatially piecewise uniform (i.e., constant in grey level value sense) for each estimated constant-value regions. For the estimation of these region partition maps, we have adopted the unsupervised Markovian framework in which parameters are automatically estimated in the least square sense. This sequential averaging allows to obtain, under our image model, an approximation of the image to be recovered in the minimal mean square sense error. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art wavelet-based denoising methods in benchmark tests.
Mignotte
Max
M
Département d'Informatique et de Recherche Opérationnelle (DIRO), Université de Montréal, Canada. mignotte@iro.umontreal.ca
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*