Computer Laboratory


Teaser figure
HDR reconstructions using different estimators discussed in the paper. The input is an exposure stack of three images captured by the Canon T1i at ISO 3200. The images were gamma-encoded for visualization (γ=2.2). We compare classical radiance estimators as well as recent noise-calibration sensitive estimators.


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.




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, 2020


This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement N◦ 725253–EyeCode).