Image Fusion using Joint Sparse Representations and Coupled Dictionary Learning

The image fusion problem consists in combining complementary parts of multiple images captured, for example, with different focal settings into one image of higher quality. This requires the identification of the sharpest areas in sets of input images. Recently, it was shown that coupled dictionary learning can successfully capture the relationships between high- and low-resolution patches in the context of single image super-resolution. In this work, to identify the sharp image patches, we propose an improved discriminative coupled dictionary learning approach using joint sparse representations in blurred and focused dictionaries. In addition, a pixel-wise processing of the boundaries (i.e., patches containing blurred and focused pixels) is proposed. The experimental results using two natural image datasets, as well as a sequence of in vivo microscopy images, show the competitiveness of the proposed method compared to state-of-the-art algorithms in terms of accuracy and computational time.