Physics-Driven Deep Panoramic Imaging for High Dynamic Range Scenes

Due to saturated regions of low dynamic range (LDR) images and large intensity changes among them, it is challenging to produce an information-enriched panoramic LDR image without visual artifacts from multiple geometrically synchronized LDR images with different exposures and piecewise overlapping fields of views for a high dynamic range (HDR) scene. Fortunately, the stitching of such images is innately a perfect scenario for the fusion of physics-driven and data-driven methods. Based on the insight, a novel neural augmented HDR panoramic stitching algorithm is proposed in this paper. Differently exposed panoramic LDR images are initialized by using a physics-driven method on top of the piecewise overlapping fields of views. They are then refined by a data-driven one, and finally merged together via a multi-scale exposure fusion algorithm to produce the desired panoramic LDR image. Experimental results validate the proposed algorithm
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The source code and trained model will be publicly available upon the acceptance..