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We introduce a neural network-based method to denoise pairs of images takenin quick succession, with and without a flash, in low-light environments. Ourgoal is to produce a high-quality rendering of the scene that preserves thecolor and mood from the ambient illumination of the noisy no-flash image, whilerecovering surface texture and detail revealed by the flash. Our networkoutputs a gain map and a field of kernels, the latter obtained by linearlymixing elements of a per-image low-rank kernel basis. We first apply the kernelfield to the no-flash image, and then multiply the result with the gain map tocreate the final output. We show our network effectively learns to producehigh-quality images by combining a smoothed out estimate of the scene's ambientappearance from the no-flash image, with high-frequency albedo detailsextracted from the flash input. Our experiments show significant improvementsover alternative captures without a flash, and baseline denoisers that useflash no-flash pairs. In particular, our method produces images that are bothnoise-free and contain accurate ambient colors without the sharp shadows orstrong specular highlights visible in the flash image.

Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments