Burst Denoising of Dark Images
This addresses the challenge of image enhancement in low-light photography for camera systems, representing an incremental improvement over existing learning-based approaches.
The paper tackles the problem of capturing images under extremely low-light conditions by proposing a deep learning framework that generates clean and colorful RGB images from dark raw images, resulting in perceptually more pleasing, sharper, and higher quality images than state-of-the-art methods.
Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional image enhancement techniques almost impossible to apply. Very recently, researchers have shown promising results using learning based approaches. Motivated by these ideas, in this paper, we propose a deep learning framework for obtaining clean and colorful RGB images from extremely dark raw images. The backbone of our framework is a novel coarse-to-fine network architecture that generates high-quality outputs in a progressive manner. The coarse network predicts a low-resolution, denoised raw image, which is then fed to the fine network to recover fine-scale details and realistic textures. To further reduce noise and improve color accuracy, we extend this network to a permutation invariant structure so that it takes a burst of low-light images as input and merges information from multiple images at the feature-level. Our experiments demonstrate that the proposed approach leads to perceptually more pleasing results than state-of-the-art methods by producing much sharper and higher quality images.