LAUSR

Multi-exposure image fusion is an important task for high dynamic range imaging. The performance of a fusion method is highly dependent on the quality of the input multi-exposure image. However, it is often difficult to obtain a multi-exposure image thoroughly covering the dynamic range of a scene. In such a situation, some areas are unclear in all the images contained by the multi-exposure image, leading to a low-visibility fusion result. To overcome this problem, Kinoshita and Kiya proposed scene segmentation-based luminance adjustment (SSLA). In SSLA, the scene is segmented into regions and the luminance of each region is adjusted so that the region has mid-level brightness. A fusion result with better visibility can be obtained by fusing the luminance adjusted multi-exposure image. Kinoshita and Kiya proposed two scene segmentation approaches for SSLA. One of them is fast but sometimes fails to segment dark areas and bright areas into different regions. The other can generate a better scene segmentation result but it requires a time-consuming iterative process. In this paper, we propose a new scene segmentation method for SSLA. The proposed scene segmentation method uses information obtained from the nonlinear luminance distribution and area occupancy of the segmented regions. A fast implementation for the proposed scene segmentation method is also described. Experimental results show that the proposed scene segmentation method is fast and stably generates good scene segmentation results.