High dynamic range imaging via truncated nuclear norm minimization of low-rank matrix

Abstract

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm using a rank minimization framework. Based on the linear dependency among irradiance maps from low dynamic range (LDR) images, we formulate ghost-free HDR imaging as a low-rank matrix completion problem. The main contribution is to solve it efficiently via the augmented Lagrange multiplier (ALM) method, where the optimization variables are updated by closed-form solutions. Experiments on real image sets show that the proposed algorithm provides comparable or even better image qualities than state-of-the-art approaches, while demanding lower computational resources.