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Deep High Dynamic Range Imaging Using Differently Exposed Stereo Images

High dynamic range (HDR) image formation from low dynamic range (LDR) images of different exposures is a well researched topic in the past two decades. However, most of the developed techniques consider differently exposed LDR images that are acquired from the same camera view point, which assumes the scene to be static long enough to capture multiple images. In this paper, we propose to address the problem of HDR imaging from differently exposed LDR stereo images using an encoder-decoder based convolutional neural network (CNN). The proposed technique does not require the LDR stereo images to be explicitly rectified and disparity corrected before merging to HDR image, unlike conventional stereo matching methods. For training and evaluation, we consider an existing benchmark dataset of HDR stereo images. The experiments have shown some interesting results in comparison to the state-of-the-art approaches. The proposed end-to-end network is found to perform equally well on LDR images that are obtained from both stereo framework and single viewpoint.