Sparse representation (SR) has been widely used in many image processing applications including image fusion. As the contents vary significantly across different images, a highly redundant dictionary is always required in the sparse model, which reduces the algorithm stability and efficiency. This paper proposes a multi-focus image fusion method based on SR with adaptive sparse domain selection (SR-ASDS). Under SR-ASDS, numerous high-quality image patches are first classified into several categories according to their gradient information, and each category is applied into training a compact sub-dictionary. At the fusion process, a corresponding sub-dictionary is adaptively selected for a given pair of source image patches. Moreover, we present a general optimization framework for the merging rule design of the SR based image fusion. Numerous experiments on both clear images and the noisy ones demonstrate that the proposed method outperforms the fusion methods which use a single dictionary, in terms of several popular objective evaluation criteria.
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