Learned Exposure Selection for High Dynamic Range Image Synthesis

High dynamic range (HDR) imaging is a photographic technique that captures a greater range of luminance than standard imaging techniques. Traditionally accomplished by specialized sensors, HDR images are regularly created through the fusion of multiple low dynamic range (LDR) images that can now be captured by smartphones or other consumer grade hardware. Three or more images are traditionally required to generate a well-exposed HDR image. This thesis presents a novel system for the fast synthesis of HDR images by means of exposure fusion with only two images required. Experiments show that a sufficiently trained neural network can predict a suitable exposure value for the next image to be captured, when given an initial image as input. With these images fed into the exposure fusion algorithm, a high-quality HDR image can be quickly generated.