Image enhancement via multiscale-exposure image fusion

The goal of image enhancement is to improve specific features or details of an image and enhance its overall visual quality. We introduce a novel image enhancement algorithm based on block-rooting processing combined with multi-scale exposure image fusion. The proposed method integrates both local and global transform domain-based feedback mechanisms for imaging applications. The core concept of the local alpha-rooting method involves applying it to disjoint blocks of varying sizes, followed by the decomposition of the weight map and multi-scale enhanced images into Gaussian and Laplacian pyramids. Fusion is achieved by multiplying the multi-scale images and their corresponding weights. A new stage is introduced to obtain a local-global estimate of high-contrast images, which is also employed in the general artificial fusion model. Computer simulations conducted on image datasets demonstrate that the new enhancement algorithm outperforms state-of-the-art techniques.