Convolution analysis operator for multimodal image fusion

Abstract Convolutional analysis operator learning, which takes advantage of the ability to extract and store overlapping blocks across training signals, has been the subject of much research in computer vision applications. The redundant filter learned by this method has the advantages of both constraining orthogonality and promoting diversity. This study, therefore, applies the convolution analysis operator to the field of image fusion and proposes a multimodal image-fusion method based on the convolution analysis operator. Experimental results show that this method performs better than the comparison methods as it not only retains the edges in the reconstructed image, but also considers the global structure of the image.