Neural Augmentation Based Panoramic High Dynamic Range Stitching
Chaobing Zheng, Yilun Xu, Weihai Chen, Shiqian Wu, Sen Zhang, Zhengguo, Li

TL;DR
This paper introduces a neural augmentation method for panoramic HDR stitching that combines physics-driven and data-driven approaches to produce artifact-free, high-quality panoramic images from multiple LDR inputs with different exposures.
Contribution
It proposes a novel neural augmentation algorithm that integrates physics-based OFOVs with data-driven refinement for improved panoramic HDR stitching.
Findings
Outperforms existing stitching algorithms in experiments.
Effectively handles saturated regions and large intensity changes.
Produces high-quality panoramic HDR images with fewer artifacts.
Abstract
Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a…
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Taxonomy
TopicsAdvanced Vision and Imaging · Video Surveillance and Tracking Methods · Industrial Vision Systems and Defect Detection
