Deep Burst Denoising

TL;DR

This paper introduces a deep neural network architecture for burst image denoising that effectively integrates multiple noisy frames to produce high-quality, low-light images, outperforming existing multi-frame methods and also enabling super-resolution.

Contribution

The paper presents a novel, flexible multiframe deep neural network architecture that improves burst denoising and generalizes to image super-resolution, advancing beyond prior techniques.

Findings

Achieves state-of-the-art denoising results on burst datasets.

Outperforms existing methods like VBM4D and FlexISP.

Demonstrates successful application to image super-resolution.

Abstract

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus 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 result in blurred images. 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, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. 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 image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution.