Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration
Abstract
A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution. The latent radiance is then estimated using Maximum Likelihood Estimation. But this requires a well-calibrated noise model of the camera, which is difficult to obtain in practice. We show that an unbiased estimation of comparable variance can be obtained with a simpler Poisson noise estimator, which does not require the knowledge of camera-specific noise parameters. We demonstrate this empirically for four different cameras, ranging from a smartphone camera to a full-frame mirrorless camera. Our experimental results are consistent for simulated as well as real images, and across different camera settings.
Cite
Text
Hanji et al. "Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-67070-2_23Markdown
[Hanji et al. "Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/hanji2020eccvw-noiseaware/) doi:10.1007/978-3-030-67070-2_23BibTeX
@inproceedings{hanji2020eccvw-noiseaware,
title = {{Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration}},
author = {Hanji, Param and Zhong, Fangcheng and Mantiuk, Rafal K.},
booktitle = {European Conference on Computer Vision Workshops},
year = {2020},
pages = {376-391},
doi = {10.1007/978-3-030-67070-2_23},
url = {https://mlanthology.org/eccvw/2020/hanji2020eccvw-noiseaware/}
}