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When taking photos in dim-light environments, due to the small amount oflight entering, the shot images are usually extremely dark, with a great dealof noise, and the color cannot reflect real-world color. Under this condition,the traditional methods used for single image denoising have always failed tobe effective. One common idea is to take multiple frames of the same scene toenhance the signal-to-noise ratio. This paper proposes a recurrent fullyconvolutional network (RFCN) to process burst photos taken under extremelylow-light conditions, and to obtain denoised images with improved brightness.Our model maps raw burst images directly to sRGB outputs, either to produce abest image or to generate a multi-frame denoised image sequence. This processhas proven to be capable of accomplishing the low-level task of denoising, aswell as the high-level task of color correction and enhancement, all of whichis end-to-end processing through our network. Our method has achieved betterresults than state-of-the-art methods. In addition, we have applied the modeltrained by one type of camera without fine-tuning on photos captured bydifferent cameras and have obtained similar end-to-end enhancements.

End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks