Physics-based Noise Modeling for Extreme Low-light Photography
This addresses the challenge of obtaining training data for low-light denoising algorithms, benefiting photography and computer vision applications, though it is incremental in improving existing methods.
The paper tackles the problem of image denoising in extreme low-light conditions by developing a physics-based noise model for CMOS photosensors, which accurately characterizes real noise structures and enables synthetic training data generation. Results show that deep neural networks trained with this model achieve accuracy on par with or better than those trained on paired real data across multiple datasets.
Enhancing the visibility in extreme low-light environments is a challenging task. Under nearly lightless condition, existing image denoising methods could easily break down due to significantly low SNR. In this paper, we systematically study the noise statistics in the imaging pipeline of CMOS photosensors, and formulate a comprehensive noise model that can accurately characterize the real noise structures. Our novel model considers the noise sources caused by digital camera electronics which are largely overlooked by existing methods yet have significant influence on raw measurement in the dark. It provides a way to decouple the intricate noise structure into different statistical distributions with physical interpretations. Moreover, our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms. In this regard, although promising results have been shown recently with deep convolutional neural networks, the success heavily depends on abundant noisy clean image pairs for training, which are tremendously difficult to obtain in practice. Generalizing their trained models to images from new devices is also problematic. Extensive experiments on multiple low-light denoising datasets -- including a newly collected one in this work covering various devices -- show that a deep neural network trained with our proposed noise formation model can reach surprisingly-high accuracy. The results are on par with or sometimes even outperform training with paired real data, opening a new door to real-world extreme low-light photography.