<p dir="ltr">We propose a hardware driven solution to high dynamic range (HDR) imaging in the form of a single metasurface lens. Our design consists of a metasurface capable of forming nine low dynamic range (LDR) sub-images of varying intensities scaling by a factor of 2 onto an imaging sensor. After synthetically verifying the functionality of our design, the metasurface is fabricated and a prototype system is constructed for real world experiments. Utilizing the experimental system, the compatibility of our extracted LDR sub- images with pre-existing exposure bracketing solutions for multi-image HDR fusion is demonstrated. The resulting HDR images are highly robust to scene motion due to the instantaneous capture of multi-exposure LDR sub-images allowing for HDR video capabilities.</p>

The construction of 3D models of real-world scenes using non-contact methods is an important problem in computer vision. Some of the more successful methods belong to a class of techniques called structured light illumination (SLI). While SLI methods are generally very successful, there are cases where their performance is poor. Examples include scenes with a high dynamic range in albedo or scenes with strong interreflections. These scenes are referred to as optically challenging environments. The work in this dissertation is aimed at improving SLI performance in optically challenging environments. A new method of high dynamic range imaging (HDRI) based on pixel-by-pixel Kalman filtering is developed. Using objective metrics, it is show to achieve as much as a 9.4 dB improvement in signal-to-noise ratio and as much as a 29% improvement in radiometric accuracy over a classic method. Quality checks are developed to detect and quantify multipath interference and other quality defects using phase measuring profilometry (PMP). Techniques are established to improve SLI performance in the presence of strong interreflections. Approaches in compressed sensing are applied to SLI, and interreflections in a scene are modeled using SLI. Several different applications of this research are also discussed.

High speed video has been a significant tool for unraveling the quantitative and qualitative assessment of phenomena that is too fast to readily observe. It was first used in 1852 by William Henry Fox Talbot to settle a dispute with reference to the synchronous position of a horse's hooves while galloping. Since that time private industry, government, and enthusiasts have been measuring dynamic scenarios with high speed video. One challenge that faces the high speed video community is the dynamic range of the sensors. The dynamic range of the sensor is constrained to the bit depth of the analog to digital converter, the deep well capacity of the sensor site, and baseline noise. A typical high speed camera can span a 60 dB dynamic range, 1000:1, natively. More recently the dynamic range has been extended to about 80 dB utilizing different pixel acquisition methods. In this dissertation a method to extend the dynamic range will be presented and demonstrated to extend the dynamic range of a high speed camera system to over 170 dB, about 31,000,000:1. The proposed formation methodology is adaptable to any camera combination, and almost any needed dynamic range. The dramatic increase in the dynamic range is made possible through an adaptation of the current high dynamic range image formation methodologies. Due to the high cost of a high speed camera, a minimum number of cameras are desired to form a high dynamic range high speed video system. With a reduced number of cameras spanning a significant range, the errors on the formation process compound significantly relative to a normal high dynamic range image. The increase in uncertainty is created from the lack of relevant correlated information for final image formation, necessitating the development of a new formation methodology. In the proceeding text the problem statement and background information will be reviewed in depth. The development of a new weighting function, stochastic image formation process, tone map methodology, and optimized multi camera design will be presented. The proposed methodologies' effectiveness will be compared to current methods throughout the text and a final demonstration will be presented. / Ph. D. / High speed video is a tool that has been developed to capture events that occur faster than a human can observe. The use and prevalence of high speed video is rapidly expanding as cost drops and ease of use increases. It is currently used in private and government industries for quality control, manufacturing, test evaluation, and the entertainment industry in movie making and sporting events. Due to the specific hardware requirements when capturing high speed video, the dynamic range, the ratio of the brightest measurement to the darkest measurement the camera can acquire, is limited. The dynamic range limitation can be seen in a video as either a white or black region with no discernible detail when there should be. This is referred to as regions of over saturation or under saturation. Presented in this document is a new method to capture high speed video utilizing multiple commercially available high speed cameras. An optimized camera layout is presented and a mathematical algorithm is developed for the formation of a video that will never be over or under saturated using a minimum number of cameras. This was done to reduce the overall cost and complexity of the setup while retaining an accurate image. The concept is demonstrated with several examples of both controlled tests and explosive tests filmed up to 3,300 times faster than a standard video, with a dynamic range spanning over 310,000 times the capabilities of a standard high speed camera. The technology developed in this document can be used in the previously mentioned industries whenever the content being filmed over saturates the imager. It has been developed so it can be scalable in order to capture extremely large dynamic range scenes, cost efficient to broaden applicability, and accurate to allow for a fragment free final image.