From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration

In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observations, we progressively approximate the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image are updated gradually and jointly in each iteration. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual quality

Synaptic and neurochemical features of calcitonin gene-related peptide containing neurons in the rat accessory optic nuclei.

Within the rodent visual system, calcitonin gene-related peptide (CGRP) is selectively expressed in neurons in the accessory optic nuclei (AON), including the dorsal terminal nucleus (DTN), lateral terminal nucleus (LTN) and medial terminal nucleus (MTN). To determine whether CGRP-immunoreactive neurons are involved in visual circuitry, electron microscopic preparations were analyzed from normal rats and rats with optic nerve transections. A co-localization analysis was also made because CGRP-labeled neurons had features of GABAergic neurons. Thus, sections were prepared for light microscopy to determine whether CGRP-containing neurons also had glutamate decarboxylase (GAD) and other markers for GABAergic neurons, such as calcium binding proteins: calbindin (CB), calretinin (CR) and parvalbumin (PV). Electron microscopy of the DTN and LTN showed CGRP-labeled somata and dendrites that were postsynaptic to axon terminals forming asymmetric synapses. Many of these axon terminals degenerated following optic nerve transection indicating that retinal ganglion cells form synapses with CGRP-labeled neurons in the AON. In the DTN, LTN and MTN, CGRP-labeled axon terminals formed symmetric synapses with unlabeled somata as well as dendritic shafts and spines. Consistent with this type of synapse being GABAergic were the co-localization data showing that about 90% of the CGRP-labeled neurons co-localized GAD in the AON. Many CGRP-labeled neurons showed immunostaining for CR (40%) whereas only a few had labeling for CB (5%). No CGRP-labeled neurons had PV. These data show that CGRP-containing neurons receive direct retinal input and represent a subpopulation of GABAergic neurons which differentially co-express calcium-binding proteins

Regenerable Antibacterial Cotton Fabric by Plasma Treatment with Dimethylhydantoin: Antibacterial Activity against S. aureus

published_or_final_versio

Anti-bacterial, anti-inflammatory and anti-adhesive coatings for urinary catherers

M.S., Biomedical Engineering -- Drexel University, 201

The Structure and Properties of Weakly Bound Clusters

In this thesis, two novel methods are introduced to advance the study of gas phase clusters. The structure similarity method is a computational technique that is able to quantify the structure difference for a pair of isomers, with a structure interpolation technique capable of finding intermediates in-between the isomer pair. A new experimental method, which couples differential mobility spectrometry with ultraviolet photodissociation spectroscopy (DMS-UVPD), is also developed and tested. Three test cases are discussed herein. These test cases showcase new theoretical techniques for mapping and visualizing potential energy surface (PES) and finding transition state (TS) structures, as well as experimental techniques of measuring UVPD spectra of DMS-MS isolated ion populations. Introduce of structure similarity, a technique developed for unsupervised machine learning (ML), enables effective domain of mapping PESs, which may subsequently be used to interpret experimental observations for systems of high geometric complexity. The experimental DMS-UVPD technique is shown capable of isolating ion species such that UVPD spectra may be recorded for characterization of analytes of interest. For the test cases described herein, these new methods provide meaningful (sometimes anti-intuitive) directions for future work. For the structure similarity method, its PES mapping capability is tested in Chapter 3 with a collection of protonated serine dimer cations, [Ser2 + H]+ to rationalize its infrared multiphoton dissociation (IRMPD) spectrum. Eventually, the spectral carrier is assigned to a non-global minimum (GM) isomer based on the partitioning information of the PES and spectral similarity. In Chapter 4, the accompanying structural interpolation method is employed to find TSs that can rationalize a regioselective alkylation reaction between a barbituric acid derivative and an alkyl-tricarbastannatrane complex. By combining the interpolation method together with chemical intuition, a total of 3 reaction channels are found, and the regioselectivity of the alkylation is identified as a kinetic effect. In Chapter 5, an acylhydrazone (AY) derivative, a photoswitch candidate, is examined using the DMS-UVPD technique. Experimentally, the protonated [AY + H]+ cation is injected into the instrument for DMS separation and laser interrogation, while theoretically, a number of neutral and protonated isomers are sampled. Eventually, separation of the ion population is observed and attributed to some ion-solvent cluster. Four isomers are found from theoretical calculation that may account for the UVPD spectr

The Power of Triply Complementary Priors for Image Compressive Sensing

Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of training images with distribution similar to the images to be recovered. On the other hand, the shallow methods which are usually unsupervised remain promising performance in many inverse problems, \eg, image compressive sensing (CS), as they can effectively leverage non-local self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various ringing artifacts due to naive patch aggregation. Using either approach alone usually limits performance and generalizability in image restoration tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-PnP based image CS problem. Extensive experimental results demonstrate that the proposed H-PnP algorithm significantly outperforms the state-of-the-art techniques for image CS recovery such as SCSNet and WNNM