Contourlet based Multi-exposure Image Fusion with Compensation for Multi-dimensional Camera Shake

Multi-exposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range by rendering multiple images captured at different exposure settings. One practical problem overlooked by existing algorithms is the compensation required for image deregistration due to possible multi-dimensional camera shake that results within the time gap of capturing the multiple exposure images. In our approach RANdom SAmple Consensus (RANSAC) algorithm is used to identify inliers of key-points identified by the Scale Invariant Feature Transform (SIFT) approach subsequently to the use of Coherent Point Drift (CPD) algorithm to register the images based on the selected set of key points. We provide experimental results on set of images with multi-dimensional (translational and rotational) to prove the proposed algorithm’s capability to register and fuse multiple exposure images taken in the presence of camera shake providing subjectively enhanced output images.