The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing. While most existing methods are based on variational models, generally derived from a Maximum A Posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we propose in this paper a model containing three terms: a patch-based sparse representation prior over a learned dictionary, the pixel-based total variation regularization term and a data-fidelity term capturing the statistics of Poisson noise. The resulting optimization problem can be solved by an alternating minimization technique combined with variable splitting. Extensive experimental results suggest that in terms of visual quality, PSNR value and the method noise, the proposed algorithm outperforms state-of-the-art methods

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Data fusion technique can produce fused images with high spatial resolution and abundant spectral information. A new image fusion algorithm based on two-dimension PCA and Curvelet transform will be proposed according to image process models specialities in this paper. First of all, we performed 2DPCA on the MS image to get the 1st principle component (PC1); then we applied Curvelet transform in Pan Image and PC1; lastly decomposition coefficients obtained was processed according to certain rules to get fused coefficients, and afterwards, we performed inverse Curvelet transform on them to acquire fused sub-images. Then we performed inverse 2DPCA transform on the other components and the fused sub-images to get fused images. Experiments will be carried out via application of multispectral and panchromatic images, and it turns out that this new algorithm can improve spatial resolution greatly while maintaining spectral information.

International audienceImage restoration plays an important role in image processing, and numerous approaches have been proposed to tackle this problem. This paper presents a modified model for image restoration, that is based on a combination of Total Variation (TV) and Dictionary approaches. Since the well-known TV regularization is non-differentiable, the proposed method utilizes its dual formulation instead of its approximation in order to exactly preserve its properties. The data-fidelity term combines the one commonly used in image restoration and a wavelet thresholding based term. Then, the resulting optimization problem is solved via a first-order primal-dual algorithm. Numerical experiments demonstrate the good performance of the proposed model. In a last variant, we replace the classical TV by the nonlocal TV regularization, which results in a much higher quality of restoration

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