Date of Award


Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Computational Science and Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Jeremy P. Bos

Committee Member 1

Timothy C. Havens

Committee Member 2

Warren F. Perger

Committee Member 3

Scott A. Kuhl


Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing time. A rich LF occupies a large chunk of memory ---order of multiple gigabytes per LF---. Also, most feature extraction techniques associated with LF postprocessing involve multi-dimensional integration that requires access to the whole LF and is usually time-consuming. Recent advancements in computer processing units made it possible to simulate realistic images using physical-based rendering software. In this work, at first, a transformation function is proposed for building a camera array (CA) to capture the same portion of LF from a scene that a standard plenoptic camera (SPC) can acquire. Using this transformation, LF simulation with similar properties as a plenoptic camera will become trivial in any rendering software. Artificial intelligence (AI) and machine learning (ML) algorithms ---when deployed on the new generation of GPUs--- are faster than ever. It is possible to generate and train large networks with millions of trainable parameters to learn very complex features. Here, residual convolutional neural network (RCNN) structures are employed to build complex networks for compression and feature extraction from an LF. By combining state-of-the-art image compression and RCNN, I have created a compression pipeline. The proposed pipeline's bit per pixel (bpp) ratio is 0.0047 on average. I show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs. In the end, using RCNN, I created a network called RefNet, for extracting a group of 16 refocused images from a raw LF. The training parameters of the 16 LFs are set to (\alpha=0.125, 0.250, 0.375, ..., 2.0) for training. I show that RefNet is 134x faster than the state-of-the-art refocusing technique. The RefNet is also superior in color prediction compared to the state-of-the-art ---Fourier slice and shift-and-sum--- methods.