Abstract
Exposure fusion is a technique for expressing high dynamic scene by fusing differently exposed images. Nowadays, although many recent methods deal with several practical issues, there is still an impractical limitation in the existing exposure fusion methods: the spatial alignment constraint. To overcome this constraint, we propose an exposure fusion method which is totally without spatial alignment assumption or spatial alignment preprocessing. We assume only two input images are available. We use locally adaptive scene contrast and exposedness as fusion criterion to fuse images in the number-balanced histogram domain. To consider spatial continuity, we use Markov Random Field to model our problem. Our experiments demonstrate that our results are comparable with existing methods no matter the input image sequence is aligned or not.