Total variation regularization-based pixel level image fusion

In this paper a total variation (TV) regularization-based approach is proposed for pixel level fusion to fuse images acquired using multiple sensors. In this approach, fusion is posed as an inverse problem and a locally affine model is used as the forward model. A total variation regularization in conjunction with principal component analysis is used iteratively to estimate the fused image. The feasibility of the proposed algorithm is demonstrated on images from visible-band and infrared as well as computed tomography (CT) and magnetic resonance imaging (MRI) sensors. The results clearly indicate the feasibility of the proposed approach.