Single shot high dynamic range imaging using piecewise linear estimators

Building high dynamic range (HDR) images by combining photographs captured with different exposure times present several drawbacks, such as the need for global alignment and motion estimation in order to avoid ghosting artifacts. The concept of spatially varying pixel exposures (SVE) proposed by Nayar et al. enables to capture in only one shot a very large range of exposures while avoiding these limitations. In this paper, we propose a novel approach to generate HDR images from a single shot acquired with spatially varying pixel exposures. The proposed method makes use of the assumption stating that the distribution of patches in an image is well represented by a Gaussian Mixture Model. Drawing on a precise modeling of the camera acquisition noise, we extend the piecewise linear estimation strategy developed by Yu et al. for image restoration. The proposed method permits to reconstruct an irradiance image by simultaneously estimating saturated and under-exposed pixels and denoising existing ones, showing significant improvements over existing approaches.

1. Introduction

The idea of using multiple differently exposed images to capture high dynamic range (HDR) scenes can be traced back to the middle of the 19th century, when the French photographer Gustave Le Gray captured a high dynamic range scene at the sea by combining two differently exposed negatives. This idea was introduced in digital photography by Mann and Picard [9] in 1995. Several methods followed, proposing different ways to combine the images [4], [11], [13], [5].