Radiance map construction based on spatial and intensity correlations between LE and SE images for HDR imaging |
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| Affiliation: | 1. Department of Mathematics and Physics, North China Electric Power University, China;2. School of Science, Communication University of China, China;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. School of Software Engineering, Tongji University, Shanghai 201804, China;2. Nanyang Technological University, Singapore;1. College of Information Science and Technology, Beijing Normal University, China;2. Lawrence Berkeley National Laboratory, United States;3. College of Information Science and Technology, Agriculture University of Hebei, China;4. School of Computer and Information Technology, Beijing Jiaotong University, China;5. School of Civil Engineering, Beijing Jiaotong University, China;1. School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, PR China;2. Department of Electronic Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli City, Taoyuan County 320, Taiwan, ROC |
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| Abstract: | HDR image was developed for the reproduction of real scenes with an acquisition of large dynamic range. In general, HDR image consists of several different exposure images according to the exposure value of a digital camera. Before the construction of a single HDR image, each input image is calibrated using CRF to convert its image data to scene radiance. In order find CRF, conventional methods require special apparatus and reference targets, or several exposure images. This paper proposes a new HDR blending algorithm that uses only dual-exposure images. The proposed algorithm is based on the least squares method, and includes spatial and intensity weighting functions. Each weighting function is used to reduce error points and improve CRF computation accuracy. In addition, a constraint is added to correct white balance in the brightness level. The rendering results show that the proposed algorithm is superior to the conventional algorithm. |
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| Keywords: | HDR Radiance map Camera response function Least squares method LE (long exposure) SE (short exposure) |
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