Deep Burst Denoising

Clement Godard, Kevin Matzen, Matt Uyttendaele; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 538-554

Noise is an inherent issue of low-light image capture, which is worsened on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in low-light situations is to increase the shutter time, allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will cause blur. Another way of gathering more light is to capture multiple short (thus noisy) frames in a “burst” and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multi-frame architecture to be a simple addition to any single frame denoising model. The resulting architecture denoises all frames in a sequence of arbitrary length. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of multi-frame image enhancement and show that our CNN architecture generalizes well to image super-resolution.