Multi-focus image fusion and image denoising are two problems in image processing. In this thesis we present new algorithms to solve these problems. In the first part, a random-walks based approach is used to fuse multi-focus image sets. Experimental results show that the proposed method outperforms many state-of-the-art techniques in both subjective and objective image quality measures. In
the second part, a self-learning SVR framework for enhancing denoised images
is presented. The preliminary results suggest that this framework is effective to
further improve the denoised images.

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