Stochastic image denoising based on Markov-chain Monte Carlo sampling

Authors:
Alexander Wong;Akshaya Mishra;Wen Zhang;Paul Fieguth;David A. Clausi
Affiliations:
Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1;Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1;Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1;Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1;Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada N2L 3G1
Venue:
Signal Processing
Year:
2011

Citing 16
Cited 2

Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Improved structure-adaptive anisotropic filter

Pattern Recognition Letters
Application of Wavelet Threshold to Image De-noising

ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Nonlocal Image and Movie Denoising

International Journal of Computer Vision
Fast communication: Adaptive bilateral filtering of image signals using local phase characteristics

Signal Processing
Image denoising with an optimal threshold and neighbouring window

Pattern Recognition Letters
Digital Image Enhancement and Noise Filtering by Use of Local Statistics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based image denoising using a Markov random field a priori model

IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

IEEE Transactions on Image Processing
On the origin of the bilateral filter and ways to improve it

IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain

IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding

IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images

IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

IEEE Transactions on Image Processing

Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation

Signal Processing
A stochastic image denoising algorithm using 3-D block filtering under a non-local means framework

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

A novel stochastic approach based on Markov-chain Monte Carlo sampling is investigated for the purpose of image denoising. The additive image denoising problem is formulated as a Bayesian least squares problem, where the goal is to estimate the denoised image given the noisy image as the measurement and an estimated posterior. The posterior is estimated using a nonparametric importance-weighted Markov-chain Monte Carlo sampling approach based on an adaptive Geman-McClure objective function. By learning the posterior in a nonparametric manner, the proposed Markov-chain Monte Carlo denoising (MCMCD) approach adapts in a flexible manner to the underlying image and noise statistics. Furthermore, the computational complexity of MCMCD is relatively low when compared to other published methods with similar denoising performance. The effectiveness of the MCMCD method at image denoising was investigated using additive Gaussian noise, and was found to achieve state-of-the-art denoising performance in terms of both peak signal-to-noise ratio (PSNR) and mean structural similarity (SSIM) metrics when compared to other published methods.