Optimal HDR reconstruction with linear digital cameras

M. Granados           B. Ajdin           M. Wand           C. Theobalt           H.-P. Seidel           H. P. A. Lensch

MPI Informatik, Universität Ulm

Input image Input image Input image Input image Input image
Input multi-exposure sequence

Ground truth Result (before smoothing) Result (after smoothing)
Ground truth (left), our result (center), and our reconstruction after noise-aware smoothing (right)


Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.


Full text (BibTeX)



Source code

Optimal exposure estimation: public_topt.tar.gz (requires Octave, and pfstools)

HDR reconstruction: public_opthdr.tar.gz (requires Matlab, and the Image processing toolbox)

Camera sensor calibration: calibration.tar.gz (requires OpenCV 2.3.x)

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