Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images

An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the Mumford–Shah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments

Development of image restoration techniques

Image denoising and image deblurring are studied as part of the thesis. In deblurring, blind deconvolution is investigated. Out of the several classes of blind deconvolution techniques, Non parametric Methods based on Image Constraints are studied at greater depth. A new algorithm based on the Iterative Blind Deconvolution(IBD) technique is developed. The algorithm makes use of spatial domain constraints of non-negativity and support. The Fourier-domain constraint may be described as constraining the product of the Fourier spectra of the image f and the Fourier spectra of the point spread function h to be equal to the convolution spectrum. Within each iteration, the algorithm switches between spatial domain and frequency domain and imposes known constraints on each. The convergence of the original IBD can be accelerated by limiting high magnitude values in frequency domain of both estimated image and point spread function. The new algorithm converges within less than 25 iterations where as the original IBD took nearly 500 iterations. Inclusion of the support constraint in the spatial domain improves quality of the restored image. Also, sum of the spatial domain values of the point spread function should be made equal to one at the end of each iteration, for preventing the loss of image intensity. PSNR values calculated for restored images show signi¯cant improvement in image quality. A PSNR of 17.8dB is obtained for the improved scheme where as it is 14.3dB for the original IBD. A new stopping criteria based on standard deviation of the image power for last k iterations is de¯ned for stopping the algorithm when it converges. In denoising, an edge retrieval technique is developed which preserves the image details along with e®ectively removing impulse noise. Noisy pixels are detected in the ¯rst phase and in the next phase those pixel values are replaced with an estimate of the actual value. For dealing with the wrong classi¯cation of edge pixels as noisy pixels, an edge retrieval technique based on pixel-wise MAD is de¯ned. This scheme retrieves the pixels which are wrongly classi¯ed as noise. The algorithm gives high PSNR values as well as very good detail preservation

Fast restoration of natural images corrupted by high-density impulse noise

In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures