A Variational Multi-Scale Model for Multi-Exposure Image Fusion.

Abstract

Multi-exposure image fusion (MEF) is the main method to obtain High Dynamic Range (HDR) images by fusing multiple images taken under various exposure values. In this paper, we propose and develop a novel variational model based on detail-base decomposition for MEF. The main idea is to incorporate the decomposition procedure and the reconstruction procedure into a unified framework, and to interact the detail information and the base information at the same time. Specifically, we make use of Tikhonov regularization to model the base layer, and we present an efficient design to obtain the detail layer, which is able to capture more detailed information effectively. Meanwhile, we incorporate multi-scale techniques to remove halo artifacts. Numerically, we apply alternating direction method of multipliers (ADMM) to solve the proposed minimization problem. Theoretically, we study the existence of the solution of the proposed model and the convergence of the proposed ADMM algorithm. Experimental examples are presented to demonstrate that the performance of the proposed model is better than that by using other testing methods in terms of visual quality and some criteria, e. g., the proposed model gives the best Natural image quality evaluator (NIQE) values with 1% - 10% improvement for real image fusion experiments and gives the best PSNR values with 13% - 20% improvement for the synthetic image fusion experiment.