Optimal HDR reconstruction with linear digital cameras

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.

1. Introduction

Most real world scenes span a radiance range that exceeds the capabilities of standard film and digital cameras. However, many applications, such as image-based lighting [4], and BRDF measurement [9], require access to the whole dynamic range of a scene. Although high dynamic range (HDR) cameras are available [15], cost and performance issues still prevent their widespread use.