We propose a novel multispectral imaging technique employing complementary notch filters instead of bandpass filters which are conventionally used in filter-based multispectral cameras. Therefore, only little power of the incoming photon signal is lost and thus the SNR of the multispectral data can be significantly improved. To validate the proposed approach, simulations of conventional bandpass filters as well as complementary notch filters are presented. To compare the resulting SNRs, the EMVA 1288 standard is adopted in such a way that it is applicable to notch filter-based multispectral cameras. It is found that the SNR can be significantly improved by using complementary filters instead of the conventional bandpass filters, especially at high spectral resolution.
In the emerging field of computational imaging, rapid prototyping of new camera concepts becomes increasingly difficult since the signal processing is intertwined with the physical design of a camera. As novel computational cameras capture information other than the traditional two-dimensional information, ground truth data, which can be used to thoroughly benchmark a new system design, is also hard to acquire. We propose to bridge this gap by using simulation. In this article, we present a raytracing framework tailored for the design and evaluation of computational imaging systems. We show that, depending on the application, the image formation on a sensor and phenomena like image noise have to be simulated accurately in order to achieve meaningful results while other aspects, such as photorealistic scene modeling, can be omitted. Therefore, we focus on accurately simulating the mandatory components of computational cameras, namely apertures, lenses, spectral filters and sensors. Besides the simulation of the imaging process, the framework is capable of generating various ground truth data, which can be used to evaluate and optimize the performance of a particular imaging system. Due to its modularity, it is easy to further extend the framework to the needs of other fields of application. We make the source code of our simulation framework publicly available and encourage other researchers to use it to design and evaluate their own camera designs.1
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