Neuromorphic Camera Guided High Dynamic Range Imaging

Jin Han, Chu Zhou, Peiqi Duan, Yehui Tang, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1730-1739


Reconstruction of high dynamic range image from a single low dynamic range image captured by a frame-based conventional camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras are able to record high dynamic range scenes in the form of an intensity map, with much lower spatial resolution, and without color. In this paper, we propose a neuromorphic camera guided high dynamic range imaging pipeline, and a network consisting of specially designed modules according to each step in the pipeline, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images. A hybrid camera system has been built to validate that the proposed method is able to reconstruct quantitatively and qualitatively high-quality high dynamic range images by successfully fusing the images and intensity maps for various real-world scenarios.

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[pdf] [supp]
author = {Han, Jin and Zhou, Chu and Duan, Peiqi and Tang, Yehui and Xu, Chang and Xu, Chao and Huang, Tiejun and Shi, Boxin},
title = {Neuromorphic Camera Guided High Dynamic Range Imaging},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}