A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Mismatch in high-rate entropy-constrained vector quantization
IEEE Transactions on Information Theory
A bond percolation-based model for image segmentation
IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
High-Quality MRC Document Coding
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
Image Segmentation Using Hidden Markov Gauss Mixture Models
IEEE Transactions on Image Processing
Parameter estimation in the spatial auto-logistic model with working independent subblocks
Computational Statistics & Data Analysis
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Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.