A large number of emerging applications, such as autonomous navigation, space exploration, surveillance, military target detection, and remote sensing, use outdoor images to monitor various activities of interest. However, images acquired under unfavorable weather conditions usually suffer from atmospheric scattering due to environmental pollution causing color-shift and low-contrast images. Dehazing is an emerging research area in the computer vision domain that intends to restore the visibility of images by eliminating the latter types of degradation. Single image dehazing, on the other hand, is more challenging since it necessitates a precise assessment of atmospheric light and transmission map. This study aims to design a dual-channel deep neural network (DCD-Net) for estimating the transmission map, further utilized to compute atmospheric light. Finally, a dehazed image is generated using the obtained atmospheric light and the transmission map. The experimental results are compared qualitatively and quantitatively with eight existing dehazing approaches based on ten metrics on six publicly available standard datasets: Foggy Road Image DAtabase, HazeRD, REalistic Single Image DEhazing, NYU-Depth, O-HAZE, I-HAZE, a few natural hazy images, and underwater images. The DCD-Net outperforms conventional techniques, according to extensive studies. Moreover, a range of relative improvements of the proposed method over other approaches is calculated for better analysis of the results. A visual internet of things (VIoT) framework employing a PYNQ-Z2 board is presented in addition to the DCD-Net. It can be applied in real-time applications, particularly in the transportation and surveillance industries. The DCD-Net is suitable for image dehazing by virtue of its multilayered structure. The VIoT uses the DCD-Net for dehazing, while the PYNQ-Z2 board serves as the central processing unit. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners —This paper is motivated by the problems occurring due to haze. Haze reduces the visibility of a scene, causing major concerns in transportation and surveillance. Existing approaches have attempted to address this issue, albeit the methods are limited. As a result, this study proposes a new dual-channel CNN model with two modules, where the first module calculates fine details of the image and the second module estimates the transmission map. Furthermore, both features are combined to produce a more reliable transmission map. The training process highly influences the resulting output of the network. Therefore, an algorithm explaining the training instructions for the practitioners is given in the appendix. The obtained transmission map is further used to estimate atmospheric light. The images are then dehazed using atmospheric light and transmission maps. In addition, we have designed a framework for image dehazing using VIoT with a PYNQ-Z2 board. Experimental results suggest that this approach gives expected results, yet, there is one limitation. This method requires a haze image and a corresponding transmission map, which is not always possible. Therefore, we will attempt to design a semi-supervised learning approach in the future.

A Dual-Channel Dehaze-Net for Single Image Dehazing in Visual Internet of Things Using PYNQ-Z2 Board

For X-ray imaging spectroscopy, high spatial resolution over a large field of view is often as important as high energy resolution, but current X-ray detectors do not provide both in the same device. Thermal Kinetic Inductance Detectors (TKIDs) are being developed as they offer a feasible way to combine the energy resolution of transition edge sensors with pixel counts approaching CCDs and thus promise significant improvements for many X-ray spectroscopy applications. TKIDs are a variation of Microwave Kinetic Inductance Detectors (MKIDs) and share their multiplexibility: working MKID arrays with 2024 pixels have recently been demonstrated and much bigger arrays are under development. In this work, we present our first working TKID prototypes which are able to achieve an energy resolution of 75 eV at 5.9 keV, even though their general design still has to be optimized. We further describe TKID fabrication, characterization, multiplexing and working principle and demonstrate the necessity of a data fitting algorithm in order to extract photon energies. With further design optimizations we expect to be able to improve our TKID energy resolution to less than 10 eV at 5.9 keV.

Highly Multiplexible Thermal Kinetic Inductance Detectors for X-Ray Imaging Spectroscopy

In recent years Microwave Kinetic Inductance Detectors (MKIDs) have emerged as one of the most promising novel low temperature detector technologies. Their unrivaled scalability makes them very attractive for many modern applications and scientific instruments. In this paper we intend to give an overview of how and where MKIDs are currently being used or are suggested to be used in the future. MKID based projects are ongoing or proposed for observational astronomy, particle physics, material science and THz imaging, and the goal of this review is to provide an easily usable and thorough list of possible starting points for more in-depth literature research on the many areas profiting from kinetic inductance detectors.

Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation

Optical and near-infrared Microwave Kinetic Inductance Detectors, or MKIDs, are low-temperature detectors with inherent spectral resolution that are able to instantly register individual photons with potentially no false counts or readout noise. These properties make MKIDs transformative for exoplanet direct imaging by enabling photon-statistics-based planet-discrimination techniques as well as performing conventional noise-subtraction techniques on shorter timescales. These detectors are in the process of rapid development, and as such, the full extent of their performance enhancing potential has not yet be quantified. 
MKID Exoplanet Direct Imaging Simulator, or MEDIS, is a general-purpose end-to-end numerical simulator for high-contrast observations with MKIDs. The simulator exploits current optical propagation libraries and augments them with a new MKIDs simulation module to provide a pragmatic model of many of the degradation effects present during the detection process. We use MEDIS to demonstrate how changes in various MKID properties affect the contrast-separation performance when conventional differential imaging techniques are applied to low-flux, short duration observations. 
We show that to improve performance at close separations will require increasing the maximum count rate or pixel sampling when there is high residual flux after the coronagraph. We predict that taking pixel yield from the value achieved by current instruments of 80% and increasing it to 100% would result in an improvement in contrast of a factor of $\sim$ 4 at 3$\lambda/D$ and $\sim$ 8 at 6$\lambda/D$. Achieving better contrast performance in this low flux regime would then require exploiting the information encoded in the photon arrival time statistics.

/pdf/first-principle-simulator-of-a-stochastically-varying-image-35unwv5u1c.pdf

First Principle Simulator of a Stochastically Varying Image Plane for Photon-Counting High Contrast Applications

Microwave Kinetic Inductance Detector (MKID) arrays are currently being developed and deployed for astronomical applications in the visible and near infrared and for sub-millimetre astronomy. One of the main challenges of MKIDs is that large arrays would exhibit a pixel yield, defined as the percentage of individually distinguishable pixels to the total number of pixels, of 75 80 %.1 Imperfections arising during the fabrication can induce an uncontrolled shift in the resonance frequency of individual resonators which end up resonating at the same frequency of a different resonator. This makes a number of pixels indistinguishable and therefore unusable for imaging. This paper proposes an approach to individually re-tune the colliding resonators in order to remove the degeneracy and increase the number of MKIDs with unique resonant frequencies. The frequency re-tuning is achieved through a DC bias of the resonator since the kinetic inductance of a superconducting thin film is current dependent and its dependence is non linear. Even though this approach has been already proposed,2 our innovative pixel design may solve two issues previously described in literature such as non-negligible electromagnetic losses to the DC bias line, and the multiplexibility of multiple resonators on a single feed-line.

Multiplexable frequency retuning of MKID arrays using their non-linear kinetic inductance

Many digital signal processing applications require a channelizer capable of moving sections of the incoming spectrum to baseband quickly and efficiently with minimal spectral leakage and signal distortion. We report the design and implementation of a 4 GHz, 4096-branch, 8-tap, 2/1 oversampled polyphase channelizer implemented on a Xilinx RFSoC. The open-source design consists of only IP cores created using Vivado HLS (C++) and IP cores available in Vivado HLx and System Generator versions 2019.1+. The channelizer was tested using a PYNQ overlay and Jupyter Notebook (Python) hosted on the RFSoC embedded CPU. The design uses 24% of the LUTs, 9% of the DSP48s, and 11% of the BRAMs on the ZCU111 RFSoC evaluation board and meets timing constraints at 512 MHz. The oversampled polyphase channelizer suppresses spectral image components below −60 dB. This design provides the first example of an oversampled polyphase channelizer running on a system on a chip architecture created without direct use of hardware description language. The presented approach leverages high-level design tools and includes source code which can be readily adapted by other researchers and development teams without the need for specialist knowledge in high-performance FPGA design.

/pdf/a-high-throughput-oversampled-polyphase-filter-bank-using-3il8dgj6wj.pdf

A High-Throughput Oversampled Polyphase Filter Bank Using Vivado HLS and PYNQ on a RFSoC

Optical and near-IR Microwave Kinetic Inductance Detectors, or MKIDs, are superconducting photon counting detectors capable of measuring the energy and arrival time of individual OIR photons without read noise or dark current. In this whitepaper we will discuss the current status of OIR MKIDs and MKID-based instruments.

Optical and Near-IR Microwave Kinetic Inductance Detectors (MKIDs) in the 2020s

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A High-Throughput Oversampled Polyphase Filter Bank Using Vivado HLS and PYNQ on a RFSoC

Optical and Near-IR Microwave Kinetic Inductance Detectors (MKIDs) in the 2020s