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Blind deconvolution refers to a class of problems of recovering a sharp version of a blurred image without any information about the blur kernel. In this paper, we propose a novel approach for blind deconvolution based on differential evolution (DE) algorithm, which is arguably one of the most powerful stochastic real-parameter optimization algorithms. Thanks to DE algorithm, various non-conjugate...
In this paper, we address a challenging problem of multi-focusing image from a single photograph taken with an uncalibrated conventional camera. In order to achieve this, we firstly derive an optical degradation model which enables us to adopt a point operation scheme to realize image multi-focusing. This scheme can effectively reduce halo artifacts in the refocused image and greatly improve the computational...
In this paper, we present a Gaussian mixture model (GMM) based method for image denoising. The method partitions an image into a set of overlapping patches, and assumes that the image patches are random variables described by a GMM. The distribution parameters of the noise free image patches are estimated from the noisy parameters which are calculated by expectation maximization (EM). Minimum mean...
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