7,532 research outputs found
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
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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
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