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.
@article{arxiv.2009.07975,
title = {Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration},
author = {Param Hanji and Fangcheng Zhong and Rafal K. Mantiuk},
journal= {arXiv preprint arXiv:2009.07975},
year = {2020}
}