If multiple images of a scene are available instead of a single image, we can use the additional information
conveyed by the set of images to generate a higher quality image. This can be done along multiple dimensions.
Super-resolution algorithms use a set of shifted and rotated low resolution images to create a high resolution
image. High dynamic range imaging techniques combine images with different exposure times to generate an
image with a higher dynamic range. In this paper, we present a novel method to combine both techniques and
construct a high resolution, high dynamic range image from a set of shifted images with varying exposure times.
We first estimate the camera response function, and convert each of the input images to an exposure invariant
space. Next, we estimate the motion between the input images. Finally, we reconstruct a high resolution, high
dynamic range image using an interpolation from the non-uniformly sampled pixels. Applications of such an
approach can be found in various domains, such as surveillance cameras, consumer digital cameras, etc.