Digital cameras under a dark illumination invoke artifacts like motion blur in a long-exposed shot or salient noise
corruption in a short-exposed (High ISO) shot. To suppress such artifacts effectively, multi-frame fusion approaches
involving the use of multiple short-exposed images has been studied actively. Moreover, it recently has been being
applied to various consumer digital cameras for the practical still-shot stabilization. However, it requires too much
computational complexities and costs in order to conduct both multiframe noise filtering and brightness/color appearance restoration well from a set of multiple input images acquired at a harsh low-light situation.
In this paper, we propose a new fusion-based low-light stabilization approach, which inputs one proper-/long-exposure blurry image as well as multiple short-exposure noisy images. First, a coarse-to-fine motion compensated noise filtering is done to get a clean image from the multiple short-exposure images. Then, online low-light image restoration is followed to obtain a good visual appearance from the denoised image using a blurry long-exposure input image. More specifically, the noise filtering is conducted by a simple block-wise temporal averaging based on a between-frame motion info, which provides a denoising result with even better detail preservation. Our simulation and real scene tests show the possibility of the proposed algorithm for fast and effective low light stabilization at a programmable computing platform.