Engineering Commons^™

Synthetic Aperture Optical Imaging Interferometric Microscopy With Improved Image Quality, Preyom K. Dey

Electrical and Computer Engineering ETDs

The resolution limit of optical microscopy can be extended by using Imaging Interferometric Microscopy (IIM), which uses a low numerical aperture (NA) objective lens to achieve resolution equivalent to that of a high-NA objective lens with multiple sub-images. Along with the resolution enhancement challenge, IIM often suffers from poor image quality. In this dissertation, several image quality improvement methods are proposed and verified with simulation and experimental results. Next, techniques to extend the resolution limit of IIM to ≤ 100nm using a low-NA objective lens are demonstrated. An experimental technique of using a grating coupler on …

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

Evaluation Of Robust Deep Learning Pipelines Targeting Low Swap Edge Deployment, David Carter Cornett

Masters Theses

The deep learning technique of convolutional neural networks (CNNs) has greatly advanced the state-of-the-art for computer vision tasks such as image classification and object detection. These solutions rely on large systems leveraging wattage-hungry GPUs to provide the computational power to achieve such performance. However, the size, weight and power (SWaP) requirements of these conventional GPU-based deep learning systems are not suitable when a solution requires deployment to so called "Edge" environments such as autonomous vehicles, unmanned aerial vehicles (UAVs) and smart security cameras.

The objective of this work is to benchmark FPGA-based alternatives to conventional GPU systems that have the …

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network, Wesley O'Quinn

Honors College Theses

Pneumonia is a life-threatening respiratory disease caused by bacterial infection. The goal of this study is to develop an algorithm using Convolutional Neural Networks (CNNs) to detect visual signals for pneumonia in medical images and make a diagnosis. Although Pneumonia is prevalent, detection and diagnosis are challenging. The deep learning network AlexNet was utilized through transfer learning. A dataset consisting of 11,318 images was used for training, and a preliminary diagnosis accuracy of 72% was achieved.

Real Vs Fake Faces: Deepfakes And Face Morphing, Jacob L. Dameron

Graduate Theses, Dissertations, and Problem Reports

The ability to determine the legitimacy of a person’s face in images and video can be important for many applications ranging from social media to border security. From a biometrics perspective, altering one’s appearance to look like a target identity is a direct method of attack against the security of facial recognition systems. Defending against such attacks requires the ability to recognize them as a separate identity from their target. Alternatively, a forensics perspective may view this as a forgery of digital media. Detecting such forgeries requires the ability to detect artifacts not commonly seen in genuine media. This work …