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

• URL: http://arxiv.org/abs/2009.07975v1
• Date: Wed, 16 Sep 2020 23:26:17 GMT
• Title: Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration
• Authors: Param Hanji, Fangcheng Zhong, Rafal K. Mantiuk
• Abstract summary: We show that an unbiased estimation of comparable variance can be obtained with a simpler Poisson noise estimator. We demonstrate this empirically for four different cameras, ranging from a smartphone camera to a full-frame mirrorless camera.
• Score: 14.715418812634939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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