  We introduce a neural network-based method to denoise pairs of images taken
in quick succession, with and without a flash, in low-light environments. Our
goal is to produce a high-quality rendering of the scene that preserves the
color and mood from the ambient illumination of the noisy no-flash image, while
recovering surface texture and detail revealed by the flash. Our network
outputs a gain map and a field of kernels, the latter obtained by linearly
mixing elements of a per-image low-rank kernel basis. We first apply the kernel
field to the no-flash image, and then multiply the result with the gain map to
create the final output. We show our network effectively learns to produce
high-quality images by combining a smoothed out estimate of the scene's ambient
appearance from the no-flash image, with high-frequency albedo details
extracted from the flash input. Our experiments show significant improvements
over alternative captures without a flash, and baseline denoisers that use
flash no-flash pairs. In particular, our method produces images that are both
noise-free and contain accurate ambient colors without the sharp shadows or
strong specular highlights visible in the flash image.
