The visual system of arthropods, called the compound eye, has distinctive features such as a wide field of view, high-speed motion detection, and infinite depth of field. These features have attracted researchers to build artificial compound eyes. However, the compound eye is limited in spatial resolution by its structural constraints such as the number and size of ommatidia that compose the compound eye. These constraints also can be found in the existing artificial compound eye. In previous work, a design method overcame these limitations and achieved resolution improvements by increasing the acceptance angle of ommatidia and using numerical optimization based on compressive sensing (CS). However, the limitation is that prior information such as a sparsifying basis is needed to solve the numerical optimization problem, and obtaining the solution to this problem is computationally time-consuming. In this paper, we propose a deep learning-based artificial compound eye. The deep learning architecture takes a measurement from the compound eye as input and learns how to reconstruct the original image. The experimental result demonstrates that the proposed deep learning approach provides improved performance in image reconstruction for the artificial compound eye.
In optical filter based compressive sensing (CS) spectrometers, an input spectrum is multiplexed and modulated by a small number of optical filters which have different sensing patterns. Then, detectors read out the modulated signals called measurements. By exploiting the CS reconstruction algorithms that utilize the measurements and the sensing patterns of optical filters, the spectrum is recovered. However, there exists a drawback on CS reconstruction algorithms. The input spectrum should be a sparse signal or be sparsely represented by a pre-determined sparsifying basis. In practice, however, the input spectrum could not be sparse or be sparsely represented by the pre-determined sparsifying basis. Therefore, the performance of spectral recovery using the CS reconstruction algorithms is varying according to the sparsity of the input spectrum and the sparsifying basis. In this paper, we implement a convolutional neural networks (CNNs) structure to reconstruct the input spectrum from the measurements of the CS spectrometers. The CNNs structure learns the way of solving the inverse problem of the underdetermined linear system. As an input of the CNNs structure, a spectrum calculated by multiplying a fixed transform matrix and the measurements is used. We investigate the reconstruction performance of the CNNs structure comparing with the CS reconstruction algorithm with different sparsifying basis. The experiment results indicate the reconstruction performance of the CNNs structure is compatible with the CS reconstruction algorithm.
In recent years, there has been an increasing interest in miniature spectrometers for research and development. Especially, filter-array-based spectrometers have advantages of low cost and portability, and can be applied in various fields such as biology, chemistry and food industry. Miniaturization in optical filters causes degradation of spectral resolution due to limitations on spectral responses and the number of filters. Nowadays, many studies have been reported that the filter-array-based spectrometers have achieved resolution improvements by using digital signal processing (DSP) techniques. The performance of the DSP-based spectral recovery highly depends on the prior information of transmission functions (TFs) of the filters. The TFs vary with respect to an incident angle of light onto the filter-array. Conventionally, it is assumed that the incident angle of light on the filters is fixed and the TFs are known to the DSP. However, the incident angle is inconstant according to various environments and applications, and thus TFs also vary, which leads to performance degradation of spectral recovery. In this paper, we propose a method of incident angle estimation (IAE) for high resolution spectral recovery in the filter-array-based spectrometers. By exploiting sparse signal reconstruction of the L1- norm minimization, IAE estimates an incident angle among all possible incident angles which minimizes the error of the reconstructed signal. Based on IAE, DSP effectively provides a high resolution spectral recovery in the filter-array-based spectrometers.
Miniature spectrometers have been widely developed in various academic and industrial applications such as bio-medical, chemical and environmental engineering. As a family of spectrometers, optical filter-array based spectrometers fabricated using CMOS or Nano technology provide miniaturization, superior portability and cost effectiveness. In filterarray based spectrometers, the resolution which represents the ability how closely resolve two neighboring spectra, depends on the number of filters and the characteristics of the transmission functions (TFs) of the filters. In practice, due to the small-size and low-cost fabrication, the number of filters is limited and the shape of the TF of each filter is nonideal. As a development of modern digital signal processing (DSP), the spectrometers are equipped with DSP algorithms not only to alleviate distortions due to unexpected noise or interferences among filters but also reconstruct the original signal spectrum. For a high-resolution spectrum reconstruction by the DSP, the TFs of the filters need to be sufficiently uncorrelated with each other. In this paper, we present a design of optical thin-film filters which have the uncorrelated TFs. Each filter consists of multiple layers of high- and low-refractive index materials deposited on a substrate. The proposed design helps the DSP algorithm to improve resolution with a small number of filters. We demonstrate that a resolution of 5 nm within a range from 500 nm to 1100 nm can be achieved with only 64 filters.
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