RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING

Abstract

High dynamic range imaging aims at creating an image with a range of intensity variations larger than the range supported by a camera sensor. Most commonly used methods combine multiple exposure low dynamic range (LDR) images, to obtain the high dynamic range (HDR) image. Available methods typically neglect the noise term while finding appropriate weighting functions to estimate the camera response function as well as the radiance map. We look at the HDR imaging problem in a denoising frame work and aim at reconstructing a low noise radiance map from noisy low dynamic range images, which is tone mapped to get the LDR equivalent of the HDR image. We propose a maximum aposteriori probability (MAP) based reconstruction of the HDR image using Gibb’s prior to model the radiance map, with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided.

Keywords

• High Dynamic Range Imaging
• Denoising
• Maximum Aposteriori Probability (MAP)
• Total Variation (TV)
• Majorize-Minimize