Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration

Published in Advances in Image Manipulation Workshop @ ECCV, 2020

Recommended citation: Param Hanji, Fangcheng Zhong and Rafał K. Mantiuk. "Noise-aware merging of high dynamic range image stacks without camera calibration". In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pages 376–391. Springer, 2020.

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.

Please view the project page for more information.