Improving Extreme Low-Light Image Denoising via Residual Learning

Taking a satisfactory picture in a low-light environment remains a challenging problem. Low-light imaging mainly suffers from noise due to the low signal-to-noise ratio. Many methods have been proposed for the task of image denoising, but they fail to work under extremely low-light conditions. Recently, deep learning based approaches have been presented that have higher objective quality than traditional methods, but they usually have high computational cost which makes them impractical to use in real-time applications or where the processing power is limited. In this paper, we propose a new residual learning based deep neural network for end-to-end extreme low-light image denoising that can not only significantly reduce the computational cost but also improve the quality over existing methods in both objective and subjective metrics. Specifically, in one setting we achieved 29x speedup with higher PSNR. Subjectively, our method provides better color reproduction and preserves more detailed texture information compared to state-of-the-art methods.