The variational method was popular and effective for image fusion in recent years, and it could constraint the geometric and gradient structures in original images according to the variational fusion equation with preserving information terms. The sparse representation was widely used for image decomposition and fused image reconstruction by combining sparse coefficients of multi-source images. Therefore, a new variational image fusion method was proposed by adding an approximated term with sparse representation model. What’s more, the sparse representation fusion was improved with the new coefficient fusion rule, and then was joined within the variational fusion frame. The proposed variational fusion method could approximate multiband images and preserve details. The fusion experiments were performed on GF-2 remote sensing images, and compared with original unimproved methods and some usual fusion methods. The results showed that the new proposed method is better than tested methods in accordance with objective evaluations and subjective visual effects.
Bandelet transform could acquire geometric regular direction and geometric flow, sparse representation could represent signals with as little as possible atoms on over-complete dictionary, both of which could be used to image fusion. Therefore, a new fusion method is proposed based on Bandelet and Sparse Representation, to fuse Bandelet coefficients of multi-source images and obtain high quality fusion effects. The test are performed on remote sensing images and simulated multi-focus images, experimental results show that the performance of new method is better than tested methods according to objective evaluation indexes and subjective visual effects.