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Single Input Stereo HDR Imaging using Deep Learning

In the domain of stereo vision, Stereo High Dynamic Range Imaging (SHDRI) is a revolutionary concept. Traditionally, monocular depth estimators had to be trained with ground truth and binocular stereo data. With the use of deep learning, this paper suggests a new SHDRI approach that uses a single image. The Left View (LV) which is the input, is given to a pretrained monocular depth estimator, which uses depth hints obtained from off-the-shelf stereo algorithms. The estimator provides us with the LV’s depth map as well as the disparity values which are then combined with the input to create the Right View (RV), resulting in the stereo pair. Using Power Law Transformation, we make multiple exposed images of both the LV and the RV. A unified and unsupervised end-to-end exposure fusion network fuses the LV and RV exposures and generates HDR images for both the views, forming the stereo HDR image pair. Additionally, we produce anaglyphs from the resulting HDR image pair, which can be employed to intuitively perceive three-dimensional structures.