Low-Light Image Enhancement for Multiaperture and Multitap Systems
Low-Light Image Enhancement for Multiaperture and Multitap Systems
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DOI：
10.1109/jphot.2016.2528122
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发表时间：
2016-04-01
影响因子：
2.4
通讯作者：
Loffeld, Otmar
中科院分区：
文献类型：
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作者：
Conde, Miguel Heredia;Zhang, Bo;Loffeld, Otmar
当只有几个或单个光子击中每个像素时，强烈的泊松噪声会大大降低图像质量。多孔径系统能够提供同时获取的同一场景的多个图像。经过配准和裁剪后，每幅孔径图像所包含的部分场景信息应该是相同的，而噪声在每幅孔径图像中是不同的。类似的情况出现在多抽头系统中，其广泛用于飞行时间成像（ToF），其中每个像素存在若干积分通道，并且其中需要若干连续采集来生成深度图像。在这种情况下，原始图像可能彼此不同，但由于它们是同一场景的图像，因此可以利用信息冗余来滤除噪声。在这项工作中，我们提出了两种不同的方式联合处理低光多孔径图像。其中之一是双边滤波的多孔径情况下的扩展，而另一个依赖于压缩感知理论，旨在从比原始噪声图像中的像素总数更少的测量中恢复无噪声图像。实验结果表明，这两种方法表现出非常接近的性能，这是远远高于以前的方法。此外，我们还表明，双边滤波也可以应用于多抽头ToF系统的原始图像，导致最终深度图像中的误差显着减少。
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.