Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration
This provides a practical solution for HDR imaging across various cameras, from smartphones to professional models, without requiring complex calibration.
The paper tackles the problem of reconstructing High Dynamic Range images from exposure stacks without needing camera-specific noise calibration, achieving comparable variance to optimal methods using a simpler Poisson noise estimator.
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.