A covariance adjustment method in compressed domain for noisy image segmentation

Noise is ubiquitous in real life and changes image acquisition and processing characteristics in an uncontrolled manner. Highly sophisticated image processing algorithms developed for clean images often malfunction when they are used for noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they have also proved to be quite sensitive to uncontrolled noise in test images. To resolve this difficulty, we propose a modified procedure to adjust covariance matrix estimates of test images. We shrink (or expand) the covariance matrix estimates of the noisy image to make them consistent with those in the codebooks. Note that the covariance matrices in the codebooks are those of the noiseless image. The novelty of this paper is that our method is equivalent to adjusting the covariance matrices of codebooks for noiseless images to be consistent wit those of noisy test images without retraining. The adjusted covariance matrices shrink (or expand) the covariance matrix estimates in the codebooks to minimize the overall minimum discrimination information distortion between test images and codebooks. To illustrate the proposed procedure, we apply it to segmenting aerial images with salt and pepper noise and with Gaussian noise. We compare our method with the median filter restoration method and the blind deconvolution method and show that our procedure has better performance than these image-restoration-based techniques in terms of both visual segmentation results and error rate. Further, we find that the suggested procedure performs almost as well as the HMGMM for clean images, which is the benchmark in comparison.