With the growing popularity of smartphones, capturing high-quality images is of vital importance to smartphones. The cameras of smartphones have small apertures and small sensor cells, which lead to the noisy images in low light environment. Denoising based on a burst of multiple frames generally outperforms single frame denoising but with the larger compututional cost. In this paper, we propose an efficient yet effective burst denoising system. We adopt a three-stage design: noise prior integration, multi-frame alignment and multi-frame denoising. First, we integrate noise prior by pre-processing raw signals into a variance-stabilization space, which allows using a small-scale network to achieve competitive performance. Second, we observe that it is essential to adopt an explicit alignment for burst denoising, but it is not necessary to integrate a learning-based method to perform multi-frame alignment. Instead, we resort to a conventional and efficient alignment method and combine it with our multi-frame denoising network. At last, we propose a denoising strategy that processes multiple frames sequentially. Sequential denoising avoids filtering a large number of frames by decomposing multiple frames denoising into several efficient sub-network denoising. As for each sub-network, we propose an efficient multi-frequency denoising network to remove noise of different frequencies. Our three-stage design is efficient and shows strong performance on burst denoising. Experiments on synthetic and real raw datasets demonstrate that our method outperforms state-of-the-art methods, with less computational cost. Furthermore, the low complexity and high-quality performance make deployment on smartphones possible.
翻译:随着智能手机越来越受欢迎,捕捉高质量的图像对于智能手机至关重要。智能手机相机具有小孔径和小传感器细胞,导致低光环境中的噪音。基于多框架的破碎,低光环境中的低亮度调整通常优于单一框架的破碎,但以更大的计算成本为基础。在本文件中,我们建议了一个高效但有效的破碎分解系统。我们采用了一个三阶段设计:噪音之前的整合、多框架对齐和多框架拆除。首先,我们先将噪声通过预处理的原始信号纳入一个低亮度稳定空间,这样可以使用小型网络来实现竞争性的图像。第二,我们发现,基于多光度框架的破碎率调整,但没有必要整合一个基于学习的方法来进行多框架调整。相反,我们采用一种常规有效的和高效的对齐方法,并把它与我们的多框架脱色网络相结合。最后,我们提出了一种分辨化战略,先先处理低亮度的低亮度信号,然后让一个小型的网络使用小型网络来实现竞争性能,然后通过一个高效的高级的计算方法,然后通过一个高清晰度的系统来显示一个高效的频率的网络的升级。