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Cultivation of a model cyanobacterium Synechocystis PCC 6803 under low light conditions in the presence of glucose and TES buffer leads to a change of the medium color from colorless to yellow. The absorption spectrum of the excreted unknown compound indicated a possible relationship to plant chlorophyll degradation products. To confirm this speculation the compound was purified by a combination of solid phase extraction and HPLC. The mass and NMR characteristics excluded its close relationship to modified tetrapyrroles, nevertheless the precise structure could not be determined by these means due to a complicated nature of the compound and its high polarity.

Low light level imaging devices have the advantage that they can produce quantitative two dimensional images. The characteristics, structure and operation of different low light level imaging devices are discussed. Cooled slow scanned charged coupled device (CCD) cameras show good detector properties; very low dark noise; low system noise; good spatial resolution; therefore seem ideal for the detection and quantitation of luminogenic assays.

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A new test set design and benchmarking approach (US Patent pending) allows a "standard observer" to assess the end-to-end image quality characteristics of video imaging systems operating in day time or low-light conditions. It uses randomized targets based on extensive application of Photometry, Geometrical Optics, and Digital Media. The benchmarking takes into account the target’s contrast sensitivity, its color characteristics, and several aspects of human vision such as visual acuity and dynamic response. The standard observer is part of the "extended video imaging system" (EVIS). The new test set allows image quality benchmarking by a panel of standard observers at the same time. The new approach shows that an unbiased assessment can be guaranteed. Manufacturers, system integrators, and end users will assess end-to-end performance by simulating a choice of different colors, luminance levels, and dynamic conditions in the laboratory or in permanent video systems installations.

<p dir="ltr">A camera's image signal processor (ISP) is responsible for taking the mosaiced and noisy image signal from the image sensor and processing it such a way that an end-result image is produced that is informative and accurately captures the scene. Real-time video capture in photon-limited environments remains a challenge for many ISP's today. In these conditions, the image signal is dominated by the photon shot noise. Deep learning methods show promise in extracting the underlying image signal from the noise, but modern AI-based ISPs are too computationally complex to be realized as a fast and efficient hardware ISP. An ISP algorithm, BLADE2 has been designed, which leverages AI in a computationally conservative manner to demosaic and denoise low-light images. The original implementation of this algorihtm is in Python/PyTorch. This Thesis explores taking BLADE2 and implementing it on a general purpose GPU via a suite of Nvidia optimization toolkits, as well as a low-level implementation in C/C++, bringing the algorithm closer to FPGA realization. The GPU implementation demonstrated significant throughput gains and the C/C++ implementation demonstrated the feasibility of further hardware development.</p>

The research explores the viability of using a small Unmanned Aerial Vehicle equipped with thermal imaging and lowlight camera to assess structural damage to steel girders. Damage assessments following natural disasters are daunting and arduous tasks that are resources intensive and dangerous. Unmanned aerial vehicles with remote sensing technology (UAV-RS) have been used in recent large-scale disaster events such as Hurricanes Katerina, Harvey, Irma, and Maria as well as others. Current assessment methods of structures include; inspectors physically conducting detailed and rapid surveys of damage with or without the assistance of special equipment, use of helicopters, satellite imagery, and new innovative methods using unmanned aerial vehicles with remote sensing technology. <div><br></div><div>The initial experiment utilized the S-BRITE facility at Purdue University. Two steel girders located at S-BRITE were used in the experiment with damages that render them structurally deficient. Experiments were conducted during hours of low visibility.</div><div><br></div><div>Most scientific studies have focused on using UAV-RS during hours of daylight. This research exploresthe use of UAV-RS during low-light conditions (i.e. early evening nautical and astronomical twilight, and night) for detecting global damage to steel girders. The goal is to present evidence for further study in the use of UAV-RS during low-light conditions for inspecting structures to include primary load bearing members. The research concluded that while the UAV-RS can detect global damage in low visibility conditions, further experiments in varying low-light conditions to include3D imaging and semi-autonomous inspectionusing computer vision are important for structural damage assessments.</div>

<p>Imaging in low light is difficult because the number of photons arriving at the image sensor is low. This is a major technological challenge for applications such as surveillance and autonomous driving. Conventional CMOS image sensors (CIS) circumvent this issue by using techniques such as burst photography. However, this process is slow and it does not solve the underlying problem that the CIS cannot efficiently capture the signals arriving at the sensors. This dissertation focuses on solving this problem using a combination of better image sensors (Quanta Image Sensors) and computational imaging techniques.</p> <p><br></p> <p>The first part of the thesis involves understanding how the quanta image sensors work and how they can be used to solve the low light imaging problem. The second part is about the algorithms that can deal with images obtained in low light. The contributions in this part include – 1. Understanding and proposing solutions for the Poisson noise model, 2. Proposing a new machine learning scheme called student-teacher learning for helping neural networks deal with noise, and 3. Developing solutions that work not only for low light but also for a wide range of signal and noise levels. Using the ideas, we can solve a variety of applications in low light, such as color imaging, dynamic scene reconstruction, deblurring, and object detection.</p>

abstract: High speed image sensors are used as a diagnostic tool to analyze high speed processes for industrial, automotive, defense and biomedical application. The high fame rate of these sensors, capture a series of images that enables the viewer to understand and analyze the high speed phenomena. However, the pixel readout circuits designed for these sensors with a high frame rate (100fps to 1 Mfps) have a very low fill factor which are less than 58%. For high speed operation, the exposure time is less and (or) the light intensity incident on the image sensor is less. This makes it difficult for the sensor to detect faint light signals and gives a lower limit on the signal levels being detected by the sensor. Moreover, the leakage paths in the pixel readout circuit also sets a limit on the signal level being detected. Therefore, the fill factor of the pixel should be maximized and the leakage currents in the readout circuits should be minimized. This thesis work presents the design of the pixel readout circuit suitable for high speed and low light imaging application. The circuit is an improvement to the 6T pixel readout architecture. The designed readout circuit minimizes the leakage currents in the circuit and detects light producing a signal level of 350µV at the cathode of the photodiode. A novel layout technique is used for the pixel, which improves the fill factor of the pixel to 64.625%. The read out circuit designed is an integral part of high speed image sensor, which is fabricated using a 0.18 µm CMOS technology with the die size of 3.1mm x 3.4 mm, the pixel size of 20µm x 20 µm, number of pixel of 96 x 96 and four 10-bit pipelined ADC’s. The image sensor achieves a high frame rate of 10508 fps and readout speed of 96 M pixels / sec. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016