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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">Imaging in low light is difficult because few photons can arrive at the sensor in a particular time interval. Increasing the exposure time is not always an option, as images will be blurry if the scenes are dynamic. If scenes or objects are moving, one can capture multiple frames with short exposure time and fuse them using carefully designed algorithms; however, aligning the pixels in adjacent frames is challenging due to the high photon shot noise and sensor read noise at low light. If the dynamic range of the scene is high, one needs to further blend multiple exposures from the frames. This blending requires removal of spatially varying noise at various lighting conditions while todays high dynamic range (HDR) fusion algorithms usually assume well illuminated scenes. Therefore, this low-light HDR imaging problem remains unsolved. </p><p dir="ltr">To address these dynamic low-light imaging problems, researches in this dissertation explore both conventional CMOS image sensors and a new type of image sensor, named quanta image sensor (QIS), develop models of the imaging conditions of interest, and propose new image reconstruction algorithms based on deep neural networks together with new training protocols to assist the learning. Researches in this dissertation target to reconstruct dynamic HDR scenes at a light level of 1 photon per pixel (ppp) or less than 1 lux illuminance.</p>

In recent years, digital media has seen incredible advancements in rendering visually stunning computer graphics scenes. Photo-realistic games, animated films, and more leave viewers blown away by the sheer beauty of their graphics. However, challenges arise when depicting dark scenes, often resulting in visual monotony and difficulty in comprehension due to insufficient detail within the scene. In order to enhance readability and visual interest of a scene, additional, artificial lights can be placed throughout a scene to enhance the aesthetic. These lights, however, must be strategically placed in order to retain an essence of darkness and maintain the delicate balance between light and dark. In this thesis, we explore an algorithm for light placement within low light, ray-traced scenes which leverages a k-means layering scheme to partition a scene and place artificial lights for artistic enhancement. Multiple scenes were generated and user feedback was collected comparing various lighting configurations for each scene, assessing the algorithm's effectiveness in improving readability and maintaining the desired level of darkness as well as how additional lighting affects the user's perception of the scene.

<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>