Input LDR sources Reconstructed LDR images Final tonemapped HDR result Figure 1: Our algorithm takes as input a sequential set of bracketed exposures of a dynamic scene (not pre-aligned) and outputs a highquality HDR result along with a reconstructed set of aligned images at each exposure. On the left are three of seven low-dynamic range (LDR) input sources taken with a standard, hand-held digital camera that have both subject and camera motion. These are followed by the outputs of our

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... at each exposure, aligned to the reference image which is not shown. Not only does our algorithm properly align the images despite the complex motion, but it also maintains the subtle lighting detail in each exposure (e.g., highlights on the hat, shading on the shirt, detail in the Christmas tree) that will contribute information to the final radiance map. On the right is our tonemapped HDR result. For a set like this with 7 input images, our algorithm takes less than 3 minutes to compute the final result at 1350 × 900 resolution. Abstract High dynamic range (HDR) imaging from a set of sequential exposures is an easy way to capture high-quality images of static scenes, but suffers from artifacts for scenes with significant motion. In this paper, we propose a new approach to HDR reconstruction that draws information from all the exposures but is more robust to camera/scene motion than previous techniques. Our algorithm is based on a novel patch-based energy-minimization formulation that integrates alignment and reconstruction in a joint optimization through an equation we call the HDR image synthesis equation. This allows us to produce an HDR result that is aligned to one of the exposures yet contains information from all of them. We present results that show considerable improvement over previous approaches.