A robust fusion scheme for multifocus images using sparse features

Multifocus image fusion is an important research topic in the computer vision and image processing field. The optical lenses that are commonly used by imaging devices, such as auto-focus cameras, have a limiting focus range. Thus, only objects within the range of distances from the devices can be captured and recorded sharply while out-of-range objects become blur. In this paper, we present a novel image fusion scheme for combining two or multiple images with different focus points to generate an all-in-focus image. We formulate the problem of fusing multifocus images as choosing most significant features from a sparse matrix produced by a newly developed robust principal component analysis (RPCA) decomposition method to form a composite feature space. Thus, the salient features presented in sharp regions can be captured and integrated into a single representation. The sparse matrix is first divided into small blocks, and standard deviation is then calculated on each block as a selection criterion. To reduce blocking artifacts, a sliding window technique is utilized to smooth the transitions between blocks. The proposed fusion scheme has been demonstrated to successfully improve fusion quality in terms of visual and quantitative evaluations. The method is also able to effectively handle both grayscale and color images.