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


Type
Conference Object
Change log
Authors
Zhong, F 
Mantiuk, RK 
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.

Description
Keywords
eess.IV, eess.IV, cs.CV
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name
Advances in Image Manipulation (ICCV workshop)
Journal ISSN
0302-9743
1611-3349
Volume Title
12537 LNCS
Publisher
Springer International Publishing
Rights
All rights reserved
Sponsorship
European Research Council (725253)
European Commission Horizon 2020 (H2020) Marie Sklodowska-Curie actions (765911)