TL;DR
This paper introduces a novel burst photography framework with a coarse-to-fine network architecture that enhances extremely dark raw images by reducing noise and recovering details, outperforming existing methods.
Contribution
It proposes a permutation invariant burst processing network that progressively enhances dark images, combining denoising and detail recovery in a unified architecture.
Findings
Produces sharper, more detailed images in low-light conditions
Outperforms state-of-the-art methods in perceptual quality
Effectively merges multiple low-light images for better results
Abstract
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 enhancement techniques almost impossible to apply. Recently, learning-based approaches have shown very promising results for this task since they have substantially more expressive capabilities to allow for improved quality. Motivated by these studies, in this paper, we aim to leverage burst photography to boost the performance and obtain much sharper and more accurate RGB images from extremely dark raw images. The backbone of our proposed framework is a novel coarse-to-fine network architecture that generates high-quality outputs progressively. 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…
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