Master of Science, The Ohio State University, 2024, Electrical and Computer Engineering

Image denoising is a fundamental problem in image processing where a high fidelity image is recovered from a noise corrupted version. Denoising is fundamental because, from the Bayesian perspective denoisers are believed to also encode information about the prior probability distribution of images. This in turn, makes denoisers a widely applicable tool in many image inverse problems like compressive sensing, deblurring, in-painting, super-resolution, etc. As a result various algorithmic approaches for denoising have been studied in the past decades. However, data-driven denoising methods, which learn to denoise images from large image datasets using deep neural networks, have demonstrated far superior performance compared to the classical algorithmic methods while having much faster inference times. The data-driven methods can be broadly classified into two categories: blind and non-blind methods. While non-blind methods require knowledge of the noise level contained within the image, blind methods which require no such information are more practical. However, the performance of many recent state-of-the-art blind denoisers depend heavily on the noise levels used during training. In more recent work, ideas of inducing scale and normalization equivariance properties in denoisers have been explored in order to make denoisers more robust to changes in noise levels from training to test data. In our work we extend upon this idea, where we introduce a method to make any given denoiser normalization equivariant using a simple idea of instance normalization, which improves the noise level robustness of the denoiser by a significantly large margin with minimal change to the underlying architecture. In this thesis, we theoretically formulate our idea from the perspective of minimizers of the Wasserstein-1 distance between empirical distributions of training and test data, and propose a more practically feasible 2-pixel approximation that yi (open full item for complete abstract)

Committee: Philip Schniter (Advisor); Lee Potter (Committee Member) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2022, Electrical Engineering

This work addresses the problem of shot noise in Time-of-Flight (ToF) camera depth sensors, which is caused by the random nature of photon emission and detection. In this paper, we derive a Bayesian denoising technique based on Maximum A Posteriori (MAP) probability estimation, implemented in the wavelet domain, which denoises (2D) depth images acquired by ToF cameras. We also propose a new noise model describing the photon noise present in the raw ToF data. We demonstrate that the raw data captured by ToF camera depth sensors follows a Skellam distribution. We test the resulting denoising technique, in the millimeter level, with real sensor data and verify that it performs better than other denoising methods described in the literature.

Committee: Keigo Hirakawa (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Electrical and Computer Engineering

Noise is present in all images captured by real-world image sensors. The distribution of real camera sensor data is well approximated by Poisson, and the estimation of the light intensity signal from the Poisson count data plays a prominent role in digital imaging. Multi-scale Poisson image denoising techniques have processed Haar frame and wavelet coefficients---being enabled by Skellam distribution analysis. Previous work has solved the minimum risk shrinkage operator (MRSO) that produces denoised wavelet coefficients with best achievable Mean Squared Error (MSE) for gray scale image. We extend the idea of MRSO to denoise color sensor data in color-opponent space, improving the quality of denoised color images. In addition, the stable representation of color is to use ratios which we denote by chromaticities. Thus we propose a new Bayes estimator for color image denoising in log-chromaticity coordinate. Using full resolution real R/G/B camera images, we verified that the proposed denoising is more stable than the state-of-art color denoising techniques, yielding higher image quality result. Furthermore, the noise parameters that characterize the level of noise in an image or video frame are required for effective denoising. We develop a novel technique to estimate the noise parameters from natural scenes by exploiting the global joint statistics across multiple video frames, which can be interpreted as a binomial random variable that is insensitive to textures and scene contents. We verify experimentally that the proposed noise parameter estimation method recovers noise parameters more accurately than the state-of-art noise parameter estimation techniques.

Committee: Keigo Hirakawa (Advisor); Russell Hardie (Committee Member); Raul Ordonez (Committee Member); Ryan Kappedal (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2004, Electrical Engineering

Wavelet-based image and video compression techniques have become popular areas in the research community. In March of 2000, the Joint Pictures Expert Group (JPEG) released JPEG2000. JPEG2000 is a wavelet-based image compression standard and predicted to completely replace the original JPEG standard. In the video compression field, a compression technique called 3D wavelet compression shows promise. Thus, wavelet-based compression techniques have received more attention from the research community. This dissertation involves further investigation of the wavelet transform in the compression of image and video signals, and a rate control method for real-time transfer of wavelet-based compressed video. A pre-processing algorithm based on the wavelet transform is developed for the removal of noise in images prior to compression. The intelligent removal of noise reduces the entropy of the original signal, aiding in compressibility. The proposed wavelet-based denoising method shows a computational speedup of at least an order of magnitude than previously established image denoising methods and a higher peak signal-to-noise ratio (PSNR). A video denoising algorithm is also included which eliminates both intra- and inter-frame noise. The inter-frame noise removal technique estimates the amount of motion in the image sequence. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion. A virtual-object video compression method is included. Object-based compression methods have come to the forefront of the research community with the adoption of the MPEG-4 (Motion Pictures Expert Group) standard. Object-based compression methods promise higher compression ratios without further cost in reconstructed quality. Results show that virtual-object compression outperforms 3D wavelet compression with an increase in compression ratio and higher PSNR. Finally, a rate-control method (open full item for complete abstract)

Committee: Yuan Zheng (Advisor) Subjects:

Doctor of Philosophy (Ph.D.), University of Dayton, 2023, Electrical and Computer Engineering

Focal plane arrays in polarimetric imaging require demodulation of the spatially modulated analyzers in order to interpolate or recover the full frame analyzer images. These micro-polarizer array (MPA) sensors are intended for real time video use, and therefore are subject to noise due to short integration time. To address these limitations together would require a joint demosaicking and denoising algorithm. We first propose a demosaicking framework to improve existing demosaicking methods, and a division-of-time Stokes simplex polarimetric imaging denoising algorithm to individually address the issues. We then develop a joint demosaicking and denoising algorithm that combines the Stokes simplex analysis with the wavelet-based denoising. The experimental results prove superior reconstruction for the joint demosaicking and denoising algorithm when compared to state-of-the-art denoising and demosaicking algorithms applied separately to the MPA.

Committee: Keigo Hirakawa (Advisor); Dan LeMaster (Committee Member); Eric Balster (Committee Member); Bradley Ratliff (Committee Member); Vijayan Asari (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2023, Electrical Engineering

This dissertation is comprised of two novel contributions. First, we propose a novel technique to determine the noise-free color at each pixel by estimating the ratio of the red, green, and blue (RGB) pixel values from their noisy version. In order to model the spatial statistics of the proportion of primary colors such as RGB components known to correspond to the human perception of color, we interpret the simplex representation of color as an Aitchison geometry. Specifically, we develop a minimum mean square error (MMSE) estimator of log-color pixel values in the wavelet representation, with Poisson as its pixel domain likelihood function. We contrast this to most existing denoising techniques that are predominantly designed for single-channel/greyscale images that are then applied to YCbCr channels independently without regard for the RGB proportionality. In the extremely low photon regime, we verify experimentally that the proposed method yields state-of-the-art color denoising performance. Second, we propose a novel image enhancement algorithm to assist with the automation of the quantification and characterization of fiber reinforced composite materials. The success of this Aitchison- and Noise2Noise-based enhancement algorithm allows for faster and more accurate classification of composite materials that are frequently used in aerospace systems. The enhancement algorithm is applied to X-ray/CT scans of composite materials and the resulting denoised frames are classified utilizing DRAGONFLY technology. It is found that the enhanced images are able to achieve superior classification accuracy as compared to unprocessed images.

Committee: Keigo Hirakawa (Advisor); Raul Ordonez (Committee Member); Kenneth Barnard (Committee Member); Stanley Chan (Committee Member); Bradley Ratliff (Committee Member) Subjects: Electrical Engineering; Engineering; Statistics

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

This dissertation addresses two separate engineering challenges: image-inverse problems and novelty detection. First, we address image-inverse problems. We review Plug-and-Play (PnP) algorithms, where a proximal operator is replaced by a call of an arbitrary denoising algorithm. We apply PnP algorithms to compressive Magnetic Resonance Imaging (MRI). MRI is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. However, when compared to other clinical imaging modalities (e.g., CT or ultrasound), the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for ac- curate, efficient reconstruction methods from undersampled datasets. We apply the PnP-ADMM algorithm to cardiac MRI and knee MRI data. For these algorithms, we developed learned denoisers that can process complex-valued MRI images. Our algorithms achieve state-of-the-art performance on both the cardiac and knee datasets. Regularization by Denoising (RED), as proposed by Romano, Elad, and Milanfar, is a powerful image-recovery framework that aims to minimize an explicit regular- ization objective constructed from a plug-in image-denoising function. Experimental evidence suggests that RED algorithms are state-of-the-art. We claim, however, that explicit regularization does not explain the RED algorithms. In particular, we show that many of the expressions in the paper by Romano et al. hold only when the denoiser has a symmetric Jacobian, and we demonstrate that such symmetry does not occur with practical denoisers such as non-local means, BM3D, TNRD, and DnCNN. To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a “score” (i.e., the gradient of a log-prior). Novelty detection is the ability for a machine learning system to detect signals that are significantly different from samples seen during training. Detecting novelties is (open full item for complete abstract)

Committee: Philip Schniter (Advisor); Rizwan Ahmad (Committee Member); Lee Potter (Committee Member) Subjects: Electrical Engineering

Master of Science (M.S.), University of Dayton, 2021, Electrical Engineering

Noise is ubiquitous to practically all types of digital imaging systems. Low light color imaging is particularly challenging as the performance of demosaicking is affected by the presence of noise. Decoupling demosaicking and denoising tasks therefore results in artifacts. In this thesis, we address the low light color imaging problem by designing demosaicking in conjunction with denoising. Representing the RGB image as a combination of luminance and chrominance components, we derive a novel Bayer CFA joint demosaicking and denoising technique, based on a combination of wavelet-based demosaicking and Aitchison geometry modeling of wavelet-logarithm. The proposed demosaicking method is a minimum mean squared error estimate of the latent luminance and chrominance Aitchison variables, whose prior distribution is modeled as Gaussian scale mixtures. The resultant joint demosaicking-denoising method yields RGB image from noisy CFA data with image contrast details preserved while attenuating the noise. We verify the effectiveness of the proposed algorithm on a new 42 megapixel raw RGB sensor data.

Committee: Keigo Hirakawa (Advisor) Subjects: Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2021, Electrical Engineering

A primary bottleneck in video processing is the readout of large sensor arrays. Typical video contains highly correlated information, which goes unexploited in traditional imaging devices. This research focuses on two revolutionary hardware designs that eliminate the need for large data handling and bypass the readout of sparse information in large arrays. First, this research proposes a novel representation for event cameras called TORE volumes and demonstrates several advantages over current methods (e.g. prioritized encoding, low computational cost, and temporal consistency). This makes the proposed method an ideal replacement for any machine learning solution that struggles to encode sparse event data into a meaningful dense tensor. TORE volumes are evaluated using several public datasets and achieve state-of-the-art performance for human pose estimation, image reconstruction, event denoising, and classification. Second, this research designs and constructs a prototype Fourier camera that compresses high-speed video in real time. Furthermore, this research evaluates several design parameters, and processing algorithms necessary to capture high-speed video including camera calibration, temporal demosaicking, and frame reconstruction. Fourier cameras perform real-time, hardware-based encoding during a single camera integration via spatial light modulation and use temporal filter arrays to sample time-related information (similar to how color filter arrays sample spectral information in standard cameras). A prototype design is constructed and evaluated against a traditional high-speed camera—achieving 4,000fps with 16× compression. The prototype design serves as an excellent proof of concept for future designs such as on-chip temporal filter arrays.

Committee: Vijayan Asari (Advisor); Keigo Hirakawa (Committee Member); Theus Aspiras (Committee Member); Bryan Steward (Committee Member) Subjects: Computer Engineering; Scientific Imaging

Master of Computer Science (M.C.S.), University of Dayton, 2021, Computer Science

In this thesis, we propose an Image Quality Assessment and Comparison metrics for Image denoising algorithms. It is well known that image denoising plays a significant role in various Image related applications. Motivated by this, we attempt to develop Image quality assessment and comparison metrics specifically targeting image denoising algorithms. We have prepared a dataset containing images of text documents with appropriate noise specifically to meet the needs of this project. Images are denoised using different algorithms and then fed into an OCR engine to obtain text, we then compare it with text obtained using ground truth images which do not have any added noise to assess denoised image quality obtained using different algorithms.

Committee: Tam Nguyen (Advisor); Yao Zhongmei (Committee Member); Luan Nguyen (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2020, Arts and Sciences: Physics

Fast imaging has long been a key direction of magnetic resonance imaging (MRI) research. Rapid imaging technology can not only shorten the measurement time, reducing the time and cost of scientific research, but also reduce patient burden to in clinical applications and reduce some errors caused by longer measurements. At present, some rapid imaging methods have been applied to clinical medicine and have achieved good results. We achieve relatively good experimental results by adjusting the parameters in the MRI sequence based on tissue parameters such as the longitudinal relaxation time, T1, and transverse relaxation time, T2. Common sequence parameters to control contrast are the flip angle, a, echo time (TE), repetition time (TR), or the addition of pre-pulses (inversion, saturation, etc). T1 mapping and T2 mapping are very common imaging methods in MRI. High- contrast T1 and T2 mapping can clearly distinguish different tissues, providing important reference data for histological study and research. The work proposed in this thesis involves combination of fast imaging and classic MRI methods, in order to develop a new mapping method for T1 and T2, so as to obtain a mapping with sufficient accuracy in as short a time as possible. The method we used in our experiment is one of the fastest current approaches: driven-equilibrium single-pulse observation of T1 or T2 (DESPOT1/T2) which is based on making multiple measurements using steady state sequences with TR < T1 or T2. In order to shorten the sampling time, we modified the DESPOT approach to use variable density sampling patterns that allow collecting as little data as possible while maintaining image quality. We also compared multiple image reconstruction methods to find the best experimental method, hoping to improve image (open full item for complete abstract)

Committee: Gregory Lee Ph.D. (Committee Chair); Leigh Smith Ph.D. (Committee Chair); Scott Holland Ph.D. (Committee Member); David Mast Ph.D. (Committee Member); Jason Woods Ph.D. (Committee Member) Subjects: Radiology

Doctor of Philosophy, The Ohio State University, 2020, Geography

Remote Sensing offers a unique perspective of our Earth and is crucial for managing its resources. Currently, there is no single satellite data product that is suitable for all applications. Satellite data are limited by their spatial, spectral, and temporal resolution. Additionally, satellite images can be affected by sensor noise and cloud cover. One of the solutions to overcome these limitations is by combining existing satellite products to minimize the drawbacks of a dataset. In this dissertation, we improve the spatial and temporal resolution of satellite data products, minimize sensor noise, and remove cloud cover from satellite images by combining data from multiple satellite sensors using deep learning methods. Deep learning has been successful in natural image superresolution, denoising, and translation and these methods perform efficiently given sufficiently large datasets and computational resources. Therefore, publicly available satellite datasets and recent computational advancements provide an ideal opportunity for applying deep learning for our tasks. In our first study, we downscale low resolution optical and thermal spectral bands of MODIS to match higher resolution NIR and Red bands. Information extraction from satellite data often requires the combined use of multiple spectral bands. Usually, the low-resolution bands are downscaled using naive interpolation methods or high-resolution bands are upscaled to create spectral indices. We train a deep learning model for downscaling MODIS spectral to create a spatially consistent MODIS dataset. Our model is compared to a state-of-the-art satellite image downscaling method and a deep learning image superresolution method. Additionally, we investigate the importance of prior natural images towards downscaling satellite images. Next, we increase the effective spatial resolution and denoise MODIS spectral bands with the help of Landsat 8 images. MODIS and Landsat 8 have similar measurement principles and (open full item for complete abstract)

Committee: Desheng Liu Dr (Advisor); Alvaro Montenegro Dr (Committee Member); Srinivasan Parthasarathy Dr (Committee Member); Rongjun Qin Dr (Committee Member) Subjects: Geographic Information Science; Geography; Remote Sensing

Master of Science in Electrical Engineering, University of Dayton, 2020, Electrical and Computer Engineering

In low-photon imaging regime, noise in image sensors are dominated by shot noise, best modeled statistically as Poisson. In this work, we show that the Poisson likelihood function is very well matched with the Bayesian estimation of the "difference of log of contrast of pixel intensities". More specifically, our work takes root in statistical compositional data analysis, whereby we reinterpret the Aitchison geometry as a multiresolution analysis in log-pixel domain. We demonstrate that the difference-log-contrast has wavelet-like properties that correspond well with human visual system, while being robust to illumination variations. We derive a denoising technique based on an approximate conjugate prior for the latent Aitchison variable that gives rise to an explicit minimum mean squared error estimation. The resulting denoising techniques preserves image contrast details that are arguably more meaningful to human vision than the pixel intensity values themselves.

Committee: Keigo Hirakawa Ph.D. (Advisor); Brad Ratliff Ph.D. (Committee Member); Vijayan Asari Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Computer Science and Engineering

In daily listening environments, the speech reaching our ears is commonly corrupted by both room reverberation and background noise. These distortions can be detrimental to speech intelligibility and quality, and also pose a serious problem for many speech-related applications, including automatic speech and speaker recognition. The objective of this dissertation is to enhance speech signals distorted by reverberation and noise, to benefit both human communications and human-machine interaction. Different from traditional signal processing approaches, we employ deep learning approaches to perform reverberant-noisy speech enhancement. Our study starts with speech dereverberation without background noise. Reverberation consists of sound wave reflections from various surfaces in an enclosed space. This means the reverberant signal at any time step includes the damped and delayed past signals. To explore such relationships at different time steps, we utilize a self-attention mechanism as a pre-processing module to produce dynamic representations. With these enhanced representations, we propose a temporal convolutional network (TCN) based speech dereverberation algorithm. Systematic evaluations demonstrate the effectiveness of the proposed algorithm in a wide range of reverberant conditions. Then we propose a deep learning based time-frequency (T-F) masking algorithm to address both reverberation and noise. Specifically, a deep neural network (DNN) is trained to estimate the ideal ratio mask (IRM), in which the anechoic-clean speech is considered as the desired signal. The enhanced speech is obtained by applying the estimated mask to the reverberant-noisy speech. Listening tests show that the proposed algorithm can improve speech intelligibility for hearing-impaired (HI) listeners substantially, and also benefit normal-hearing (NH) listeners. Considering the different natures of reverberation and noise, we propose to perform speech enhancement using a two-stage (open full item for complete abstract)

Committee: DeLiang Wang (Advisor); Eric Fosler-Lussier (Committee Member); Eric Healy (Committee Member) Subjects: Computer Science; Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2020, EECS - Computer and Information Sciences

Learning-based computational methods offer significant benefits to the field of genomics since studying the genome often requires gathering large datasets for which classical statistical methods of analysis can fall short. In this work we propose a machine learning pipeline for denoising and upsampling high resolution Hi-C data: an experiment which measures the genome-wide 3D spatial interactions (loops) of chromatin inside the cell nucleus. Our models are able to recover the true chromatin loops with high confidence from only ~1/40th of the usual billions of sequencing reads required. This level of precision will allow for re-analyses of existing Hi-C datasets and future experiments at a fraction of the cost. Limiting barriers to entry in studying chromatin organization can help increase the speed of progress in understanding gene regulation and how it influences development and disease.

Committee: Jing Li PhD (Advisor); Fulai Jin PhD (Committee Member); Michael Lewicki PhD (Committee Member) Subjects: Bioinformatics; Computer Science

Doctor of Philosophy, The Ohio State University, 2020, Computer Science and Engineering

Removing noise and recovering signals is a fundamental task in the area of data analysis. Noise is everywhere: In supervised learning, samples in the training set can be mislabeled. In road network reconstruction, wrong trajectories could come from the low sampling rate and bad GPS signals. Despite the fact that much work has been done in this area, the problem remains challenging because real-life noise is complicated to model and usually little knowledge of the ground truth is available. On the other hand, in many situations, assuming that the data presumably samples from a hidden space called ground truth, different types of noise such as Gaussian and/or ambient noise can be associated with it. For all types of noise, signals should prevail in density, which means that the data density should be higher near the ground truth. My work deals with such noisy data in two contexts. In the first scenario, we consider eliminating noise from a point cloud data sampled from a hidden ground truth K in a metric space. General denoising methods such as deconvolution and thresholding require the user to choose parameters and noise models. We first give a denoising algorithm with one parameter and assume a very general sampling condition. We provide the theoretical guarantee for this algorithm and argue that the one parameter cannot be avoided. We then propose a parameter-free denoising algorithm with a sampling condition that is slightly stronger. We show our method performs well on noisy uniform/adaptive point clouds by experiments on a 2D density field, 3D models, and handwritten digits. In the second scenario, we consider reconstructing a hidden graph from a noisy sample. Recently, a method based on Discrete Morse theory and persistent homology finds its applications in multiple areas, for example, reconstructing road networks from GPS trajectories and extracting filamentary structures from cosmology data. However, little theoretical analysis exists for the Discrete (open full item for complete abstract)

Committee: Tamal Dey (Advisor); Yusu Wang (Advisor); Han-Wei Shen (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2020, Computer Science and Engineering

Automatic speech recognition has become a part and parcel of everyday life. From the early 2000s, deep neural networks connected to hidden markov models (DNN-HMMs) have single-handedly pushed the performance of clean speech recognition systems to human level. Since then, the simple feedforward architectures have evolved to more sophisticated ones, like convolutional neural networks, which can correlate complex patterns to phones, and recurrent neural networks, which use horizontal connections to better utilize past (and future) context. These modern neural networks have pushed the boundaries of automatic speech recognition, lowering word error rates drastically. However, the improvement in performance comes at the cost of higher train times and decoding speeds because these neural networks are bulky, i.e. they have a large number of trainable parameters compared to feedforward neural networks. They are also intensely data-driven, i.e. high performance accuracy can only be achieved with a large set of training examples. The straightforwardness and simplicity of the feedforward architecture makes it a powerful contender for real-time speech recognition. There has been a growing amount of research which transfers knowledge from a cumbersome, high complexity "teacher" network to a simpler "student" network with lower complexity. The main focus is to make feedforward neural networks imitate the behavior of convolutional or recurrent neural networks. In the course of this dissertation, I am going to walk through some results on knowledge transfer from recurrent and convolutional neural nets to feedforward neural nets in the realm of speech enhancement and multilingual speech recogntion. Spectral mapping is a form of speech denoising which explicitly maps noisy speech to clean speech. In the first part of this dissertation, I describe a plug-and-play spectral mapping system that can be used as a front end feature denoiser for any speech recognition system. The feat (open full item for complete abstract)

Committee: Eric Fosler-Lussier (Advisor); Deliang Wang (Committee Member); Micha Elsner (Committee Member) Subjects: Acoustics; Computer Engineering; Computer Science

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Electrical and Computer Engineering

This dissertation is comprised of four novel contributions. First, we propose new implementations of Monte-Carlo-based bilateral filter and non-local means whose per-pixel complexity is approximately invariant to the color dimension, window size, and block size. We reduce complexity by combining the random filtering of multiple color channels that approximate the non-linear behavior of the bilateral filter into a single convolution operation. We extend this work to a non-linear filter called Non-Local Means. In the second part, we propose "convolutional distance transform"-- efficient implementations of distance transform. Specifically, we leverage approximate minimum functions to rewrite the distance transform in terms of convolution operators, reducing the complexity to N logN. Third, we propose a novel method for multi-frame image denoising in mobile phones. We developed a method to register noisy image frames by estimating the camera motion using both image and inertial measurements. Lastly, we develop a new framework for multi-frame image denoising using noisy image statistics of one frame to design an optimal denoising filter for the second frame. The algorithm is provably optimal in minimum mean squared error estimation sense as well as in wavelet structural similarity metric sense.

Committee: Keigo Hirakawa (Advisor); Eric Balster (Committee Member); Raúl Ordóñez (Committee Member); Ju Shen (Committee Member) Subjects: Electrical Engineering

Master of Science in Computer Engineering, University of Dayton, 2016, Electrical and Computer Engineering

In this thesis, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on Non-Local Means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patched, within a given search window. The weights are based on the patch intensity vector dis- tances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally de- manding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here we propose a novel Recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for cop- mutationally savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current-frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality, as well as processing speed.

Committee: Russell Hardie Ph.D (Advisor); Vijayan Asari Ph.D (Committee Member); John Loomis Ph.D (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Doctor of Philosophy, Case Western Reserve University, 2017, Biomedical Engineering

Intracoritcal microelectrodes recording technology has significantly contributed to scientific researches on Central Nervous System (CNS), and also enabled exciting new applications like Brain-Machine-Interface. The attractiveness of the technology lies on their ability to record single neuron's firing events, i.e. action potentials. Together with advanced signal processing algorithms, researchers could accurately detect when an action potential occurs, as well as attribute each action potential to its appropriate neurons. However, engineering application of intracortical microelectrode recording technology is currently hindered by the inability to reliably record chronic neuronal signals. The precise mechanism of long term recording failure is still not clear, but CNS immune response toward implanted foreign body has been commonly hypothesized as a main cause. Currently, the “gold standard” method for assessing CNS immune response is immunohisotochemistry (IHC), which is a lethal method where the implanted electrode needs to be explanted and host animal needs to be sacrificed. This results in several limitations of this method, including but not limited to, the inability to perform predictive experiment (i.e. assess the degree of CNS immune response before experiment ends) rather than purely explanatory experiment (i.e. assess the degree of CNS immune response after experiment ends). This dissertation has two independent goals. First, an alternative non-lethal method to assess CNS immune response was investigated. Electrical Impedance Spectroscopy (EIS) has been preliminarily reported as sensitive to the degree of CNS immune response surrounding the measured electrode. Unfortunately, the very limited number of reports on this phenomenon has been incomplete and knowledge of microelectrode's chronic EIS characteristics is missing. In addition, no attempt has been made to use EIS's potential of reflecting CNS immune response to correlate with electrode recording pe (open full item for complete abstract)

Committee: Dawn Taylor (Advisor); Jeffrey Capadona (Committee Chair) Subjects: Engineering