Image tone mapping based on clustering and human visual system models

Natural scenes generally have very high dynamic range (HDR) which cannot be captured in the standard dynamic range (SDR) images. HDR imaging techniques can be used to capture these details in both dark and bright regions, and the resultant HDR images can be tone mapped to reproduce them on SDR displays. To adapt to different applications, the tone mapping operator (TMO) should be able to achieve high performance for diverse HDR scenes. In this paper, we present a clustering-based TMO by embedding human visual system models that function effectively in different scenes. A hierarchical scheme is applied for clustering to reduce the computational complexity. We also propose a detail preservation method by superimposing the details of original HDR images to enhance local contrasts, and a color preservation method by limiting the adaptive saturation parameter to control the color saturation attenuating. The effectiveness of our method is assessed by comparing with state-of-the-art TMOs quantitatively on large-scale HDR datasets and qualitatively with a group of subjects. Experimental results of both objective and subjective evaluations show that the proposed method achieves improvements over the competing methods in generating high quality tone-mapped images with good contrast and natural color appearance for diverse HDR scenes.