Multichannel Hadamard Transform Raman Microscopy

1990 ◽  
Vol 44 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Patrick J. Treado ◽  
Michael D. Morris
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Multiplexing ◽  
Raman Microscopy ◽  
Hadamard Transform ◽  
Multichannel Detection ◽  
Spatial Multiplex

Spatial multiplexing is combined with multichannel detection in a Hadamard transform Raman microscope which provides 127 × 128 pixel images with 12 cm−1 spectral resolution. Spatial resolution of 0.6 μm per pixel has been achieved. A spatial multiplex advantage of better that 104 is demonstrated. Instrumental design details and spectroscopic images are presented.

Single Layer Graphene for Estimation of Axial Spatial Resolution in Confocal Raman Microscopy Depth Profiling

2018 ◽  
Vol 91 (1) ◽  
pp. 1049-1055 ◽  
Author(s):  
Carol Korzeniewski ◽  
Jay P. Kitt ◽  
Saheed Bukola ◽  
Stephen E. Creager ◽  
Shelley D. Minteer ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Depth Profiling ◽  
Single Layer ◽  
Raman Microscopy ◽  
Single Layer Graphene ◽  
Confocal Raman Microscopy ◽  
Layer Graphene ◽  
Confocal Raman

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

Postscript

2020 ◽  
pp. 403-404
Author(s):  
Timothy P. L. Roberts ◽  
James W. Wheless ◽  
Andrew C. Papanicolaou
Keyword(s):  
Magnetic Resonance Imaging ◽  
Electrical Conductivity ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Inverse Modeling ◽  
Oscillatory Activity ◽  
Electric Potentials ◽  
Spatial Source

As is evident from the scientific chapters of this book, the technology of magnetoencephalography offers a combination of spatial, temporal, and spectral resolution, unique among neuroimaging technologies. While functional magnetic resonance imaging (fMRI) accommodates spatial resolution, it lacks the millisecond resolution (because of the reliance on a slow hemodynamic response) to identify subtle latency shifts, or the specificity to distinguish theta- versus alpha- versus gamma-band oscillatory activity. While electroencephalography (EEG) offers the needed temporal resolution, it fails to adequately localize brain sources, owing to the physics of inverse modeling and the dependence of scalp electric potentials on tissue electrical conductivity. Thus, although fMRI may see “activity,” it cannot characterize important attributes of its nature. Conversely, EEG may detect “anomalies” but not be able to attribute them to a particular spatial source....

Image processing method to improve AOTF spectral resolution and spatial resolution

2016 ◽  
Vol 63 (21) ◽  
pp. 2203-2210
Author(s):  
Rui Zhang ◽  
Kewu Li ◽  
Yuanyuan Chen ◽  
Yaoli Wang ◽  
Zhibin Wang
Keyword(s):  
Image Processing ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Processing Method ◽  
Image Processing Method

scholarly journals The Structure and Kinematics of the Ionized Gas In Centaurus A

1987 ◽  
Vol 127 ◽  
pp. 417-418
Author(s):  
J. Bland ◽  
K. Taylor ◽  
P. D. Atherton
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Line Of Sight ◽  
Rotating Disc ◽  
Ionized Gas ◽  
Dust Lane ◽  
Data Cubes ◽  
Fabry Perot ◽  
Centaurus A

The TAURUS Imaging Fabry-Perot System (Taylor & Atherton 1980) has been used with the IPCS at the AAT to observe the ionized gas within NGC 5128 (Cen A) at [NII]λ6548 and Hα. Seven independent (x, y,λ) data cubes were obtained along the dust lane at high spectral resolution (30 km/s FWHM) and at a spatial resolution limited by the seeing (~1″). From these data, maps of the kinematics and intensities of the ionized gas were derived over a 420″ by 300″ region. The maps are the most complete to date for this object comprising 17500 and 5300 fitted spectra in Ha and [NII]λ6548 respectively. The dust lane system is found to be well understood in terms of a differentially rotating disc of gas and dust which is warped both along and perpendicular to the line-of-sight.

Hyperspectral Raman Imaging Using a Spatial Heterodyne Raman Spectrometer with a Microlens Array

2020 ◽  
Vol 74 (8) ◽  
pp. 921-931 ◽  
Author(s):  
Ashley Allen ◽  
Abigail Waldron ◽  
Joshua M. Ottaway ◽  
J. Chance Carter ◽  
S. Michael Angel
Keyword(s):  
Raman Spectra ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Diffraction Gratings ◽  
Complementary Metal Oxide Semiconductor ◽  
Microlens Array ◽  
Raman Imaging ◽  
Oxide Semiconductor ◽  
Raman Spectrometer ◽  
A Charge

A new hyperspectral Raman imaging technique is described using a spatial heterodyne Raman spectrometer (SHRS) and a microlens array (MLA). The new technique enables the simultaneous acquisition of Raman spectra over a wide spectral range at spatially isolated locations within two spatial dimensions ( x, y) using a single exposure on a charge-coupled device (CCD) or other detector types such as a complementary metal-oxide semiconductor (CMOS) detector. In the SHRS system described here, a 4 × 4 mm MLA with 1600, 100 µm diameter lenslets is used to image the sample, with each lenslet illuminating a different region of the SHRS diffraction gratings and forming independent fringe images on the CCD. The fringe images from each lenslet contain the fully encoded Raman spectrum of the region of the sample “seen” by the lenslet. Since the SHRS requires no moving parts, all fringe images can be measured simultaneously with a single detector exposure, and in principle using a single laser shot, in the case of a pulsed laser. In this proof of concept paper, hyperspectral Raman spectra of a wide variety of heterogeneous samples are used to characterize the technique in terms of spatial and spectral resolution tradeoffs. It is shown that the spatial resolution is a function of the diameter of the MLA lenslets, while the number of spatial elements that can be resolved is equal to the number of MLA lenslets that can be imaged onto the SHRS detector. The spectral resolution depends on the spatial resolution desired, and the number of grooves illuminated on both diffraction gratings by each lenslet, or combination of lenslets in cases where they are grouped.

scholarly journals Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

2020 ◽  
Vol 12 (6) ◽  
pp. 993 ◽  
Author(s):  
Chen Yi ◽  
Yong-qiang Zhao ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
Resolution Enhancement ◽  
Multispectral Image ◽  
Observation Model ◽  
Multispectral Images ◽  
Spectral Matrix ◽  
Spectral Bands ◽  
Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

scholarly journals Infrared and Raman Microscopy: Pushing the Limits of Spatial Resolution

2009 ◽  
Vol 15 (S2) ◽  
pp. 562-563 ◽  
Author(s):  
T Tague
Keyword(s):  
Spatial Resolution ◽  
Raman Microscopy

Extended abstract of a paper presented at Microscopy and Microanalysis 2009 in Richmond, Virginia, USA, July 26 – July 30, 2009

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