Low-Light Image Enhancement for Multiaperture and Multitap Systems

Intense Poisson noise drastically degrades image quality when only a few or when a single photon hits each pixel. Multiaperture systems are able to provide multiple images of the same scene, which are acquired simultaneously. After registration and cropping, the partial scene information contained in each aperture image should be the same, while the noise will be different in each one. A similar case arises in multitap systems, which are widely used in Time-of-Flight imaging (ToF), where several integration channels per pixel exist and where several sequential acquisitions are needed to generate a depth image. In this case, raw images might be different from each other, but still, since they are images of the same scene, information redundancy can be exploited to filter out the noise. In this work, we propose two different ways of joint processing of low-light multiaperture images. One of them is an extension of bilateral filtering to the multiaperture case, while the other relies on the compressive sensing theory and aims to recover a noiseless image from fewer measurements than the total number of pixels in the original noisy images. Experimental results show that both methods exhibit very close performance, which is much higher than those of previous methods. Additionally, we show that bilateral filtering can also be applied to the raw images of multitap ToF systems, leading to a significant error reduction in the final depth image.