Physics-Driven Deep Panoramic Imaging for High Dynamic Range Scenes
Chaobing Zheng, Yilun Xu, Weihai Chen, Shiqian Wu, Zhengguo G. Li · 2023
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 algorithm11The source code and trained model will be publicly available upon the acceptance..