Robust High Dynamic Range Imaging by Rank Minimization

This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.

1 Introduction

Radiance of a natural scene typically has a wider dynamic range than the dynamic range of cameras that can be recorded. One solution to obtain a full radiance map is to capture multiple low dynamic range (LDR) images at different exposures and merge them to reconstruct a high dynamic range (HDR) image. Over the past decades, there have been a lot of works targeting high dynamic range imaging. Representative works include [1], [2], [3], [4], [5], [6], [7], [8], [9].