Image Fusion-Based Tone Mapping Using Gaussian Mixture Model Clustering
Wang-Un Lee, Seung Park, Sung-Jea Ko · 2020
Tone mapping (TM) algorithms convert high dynamic range (HDR) images into low dynamic range (LDR) images to represent on conventional display devices. Most TM methods compress the dynamic range of input HDR images by using a global transformation function (TF), and then improve local detail by applying contrast enhancement techniques. However, these approaches often fail to restore local detail lost in the dynamic range compression. To solve this problem, we propose a novel image fusion-based TM method. We use Gaussian mixture model clustering algorithm to estimate the dark and bright distributions in the luminance histogram of the input HDR image. Then, we generate two LDR images using two locally-adaptive TFs obtained by the components of each distribution. Finally, the output image is produced by the image fusion technique employing a brightness weight and a local contrast weight. The experimental results show that the proposed algorithm achieves high performance compared to state-of-the-art methods in terms of detail preservation and brightness adjustment.