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

• URL: http://arxiv.org/abs/2012.05116v2
• Date: Wed, 14 Apr 2021 19:26:22 GMT
• Title: Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
• Authors: Zhihao Xia, Micha\"el Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
• Abstract summary: 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.
• Score: 51.74566709730618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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