High dynamic range imaging with short- and long-exposures based on artificial remapping using multiscale exposure fusion

Abstract

High dynamic range imaging (HDRI) is an excellent high-quality image acquisition technique, which can reflect real human visual characteristics from one (or several) captured low dynamic range (LDR) image. However, the input LDR image only provides partial information of the scene. Besides, in traditional HDRI methods that require multiple captured images as input, field of view errors can be induced, which will be difficult to apply it to the emerging image acquisition systems. Here, we propose a novel HDRI method that reconstructs an HDR image from only a pair of short- and long-exposure images based on artificial remapping using multi-scale exposure fusion. Firstly, we introduce a simulated exposure model called artificial remapping to synthesize a multi-exposure image sequence from the input LDR image pairs. Then, weighting maps of the sequence for fusion can be obtained according to the evaluation factors of contrast, saturation, as well as improved exposedness. Finally, we utilize the pyramid based multiscale exposure fusion framework to integrate them into an enhanced HDR image. Comparative experiments, fully implemented on some source images, have been demonstrated that better performance can be realized compared with some competing methods in qualitative and quantitative evaluation. Note that the operation of the proposed method is simple yet effective, which is easy to popularize. The method thus can be potentially applied to the emerging image acquisition systems where two images are captured simultaneously by two image sensors or by one image sensor with a pair of short- and long-exposure setting.