SPECTRAL MEASUREMENT AND CLASSIFICATION IN THE ERA OF BIG DATA

The measurement and classification of light is essential across many scientific disciplines. Devices used to measure light range from the highly precise scanning spectroradiometers to the more practical compact multichannel filter-array type imaging sensors and the ubiquitous RGB pixel. While there have been numerous successful efforts to reconstruct spectrum from RGB, RGB-to-spectrum reconstruction has historically been limited to natural scenes and other edge cases under strict constraints. However, information theory and recent advances in deep learning have shed new light on the vast amount of redundancy contained within data collected in the natural world, including light. In this paper, we will investigate how analytic methods can help map high dimensional spectra data to a low-dimensional feature space with minimal inductive bias. Through a better understanding of the intrinsic dimension of the data, we can use the features expressed in this representation to exploit regularities and make tasks like data compression, measurement and classification more efficient. The aim of this analysis is to help inform how and when low-dimensional representation of spectra is useful in practice for designing compact sensors as well as for lossy data compression and robust classification.

Plasmonic color filter array based visible light spectroscopy

Compared with traditional Fabry–Perot optical filters, plasmonic color filters could greatly remedy the complexity and reduce the cost of manufacturing. In this paper we present end-to-end demonstration of visible light spectroscopy based on highly selective plasmonic color filter array based on resonant grating structure. The spectra of 6 assorted samples were measured using an array of 20 narrowband color filters and detected signals were used to reconstruct original spectra by using new unmixing algorithm and by solving least squares problem with smoothing regularization. The original spectra were reconstructed with less than 0.137 root mean squared error. This works shows promise towards fully integrating plasmonic color filter array in imagers used in hyperspectral cameras.

Joint demosaicing of colour and polarisation from filter arrays

This article considers the joint demosaicing of colour and polarisation image content captured with a Colour and Polarisation Filter Array imaging system. The Linear Minimum Mean Square Error algorithm is applied to this case, and its performance is compared to the state-of-theart Edge-Aware Residual Interpolation algorithm. Results show that the LMMSE demosaicing method gives statistically higher scores on the largest tested database, in term of peak signal-to-noise ratio relatively to a CPFA-dedicated algorithm.

Data-Driven Convolutional Model for Digital Color Image Demosaicing

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.

3D conformal bandpass millimeter-wave frequency selective surface with improved fields of view

Conventional planar frequency selective surfaces (FSSs) are characterized in the far-field region and they are sensitive to the incidence angle of impinging waves. In this paper, a spherical dome FSS is presented, aiming to provide improved angular stable bandpass filtering performance as compared to its planar counterpart when the FSS is placed in the near-field region of an antenna source. A comparison between the conformal FSS and a finite planar FSS is presented through simulations at the frequency range between 26 to 40 GHz in order to demonstrate the advantages of utilizing the conformal FSS in the near-field. The conformal FSS is 3D printed and copper electroplated, which leads to a low-cost and lightweight bandpass filter array. Placing it in the near-field region of a primary antenna can be used as radomes to realize compact high-performance mm-wave systems. The comparison between simulated and measured conformal FSS results is in good agreement. The challenges that arise when designing, manufacturing, and measuring this type of structure are reported and guidelines to overcome these are presented.

Design of mid-infrared filter array based on plasmonic metal nanodiscs array and its application to on-chip spectrometer

Mid-infrared wavelengths are called the molecular fingerprint region because it contains the fundamental vibrational modes inherent to the substances of interest. Since the mid-infrared spectrum can provide non-destructive identification and quantitative analysis of unknown substances, miniaturized mid-infrared spectrometers for on-site diagnosis have attained great concern. Filter-array based on-chip spectrometer has been regarded as a promising alternative. In this study, we explore a way of applying a pillar-type plasmonic nanodiscs array, which is advantageous not only for excellent tunability of resonance wavelength but also for 2-dimensional integration through a single layer process, to the multispectral filter array for the on-chip spectrometer. We theoretically and experimentally investigated the optical properties of multi-periodic triangular lattices of metal nanodiscs array that act as stopband filters in the mid-infrared region. Soft-mold reverse nanoimprint lithography with a subsequent lift-off process was employed to fabricate the multispectral filter array and its filter function was successfully extracted using a Fourier transform infrared microscope. With the measured filter function, we tested the feasibility of target spectrum reconstruction using a Tikhonov regularization method for an ill-posed linear problem and evaluated its applicability to the infrared spectroscopic sensor that monitors an oil condition. These results not only verify that the multispectral filter array composed of stopband filters based on the metal nanodiscs array when combined with the spectrum reconstruction technique, has great potential for use to a miniaturized mid-infrared on-chip spectrometer, but also provide effective guidance for the filter design.

Color star tracking II: matching

A novel matching algorithm is presented that can identify stars using raw images of the sky obtained from a CMOS color filter array detector. The algorithm combines geometric information with amplitude ratios calculated from the red, green, and blue color color channels. Conventional algorithms that match stars based solely on inter-star geometry (and sometimes relative brightness), typically require three or more stars for a confident star match. In contrast, the presented algorithms are able to find matches with only two imaged stars in most regions of the sky. The necessary catalog preparation and a simple star-pair matching algorithm based on combined color intensity ratios and the angular spacing are discussed. Results from a large set of simulation trials and initial results from sensor field testing are presented.