Burst Denoising

Burst denoising aims to improve image quality by combining multiple noisy images (a "burst") captured in quick succession. Current research focuses on developing efficient algorithms, often employing deep learning architectures like convolutional neural networks and recurrent neural networks (including LSTMs and BiLSTMs), to effectively align and fuse burst images while mitigating noise. These advancements leverage techniques such as variance stabilization and multi-frequency processing to achieve superior denoising performance compared to single-image methods, with a particular emphasis on reducing computational cost for real-time applications, especially on mobile devices. The resulting improvements in image quality have significant implications for mobile photography and other applications requiring high-quality images from low-light or challenging conditions.