Multiexposure Estimation and Fusion Based on a Sparsity Exposure Dictionary

Abstract

Multiexposure image fusion (MEF), which is an emerging hot research topic, aims to fuse multiexposure images into a high-quality low dynamic range (LDR) image. In this article, we propose a novel exposure estimation-based MEF method with sparse decomposition and a designed sparsity exposure dictionary (SED). First, in order to represent more elementary information of multiexposure images, SED is trained by underexposed and overexposed images and constructed by the K-means-based algorithm (K-SVD) algorithm. Next, the SED is applied to construct exposure estimation maps by the number of atoms of the image patch, which is obtained by sparse decomposition. When the exposure estimation maps are obtained, a novel fusion framework that uses these maps to fuse multiexposure images is presented. In this framework, we employ the exposure estimation maps and an adaptive guided filter to construct the final fusion decision maps. The two factors of guided filter are adaptively determined without manual interference to obtain an optimal representation effect for multiexposure image sequences with different sizes and number. Finally, a fused image is generated via the pyramid merging method. The proposed fusion method is conducted on a series of multiexposure image sequences and compared to seven popular MEF methods. The experimental results clearly demonstrate that the proposed method outperforms the state-of-the-art methods in terms of subjective visual and quantitative evaluations.