Download Compressive Sensing Microscopy for Nanomaterial Analysis Abstract Compressive Hyperspectral Microscopy

Graduate Research
Compressive Sensing Microscopy for Nanomaterial Analysis
Liyang Lu, Yun Li, Yibo Xu, Jianbo Chen, and Kevin F. Kelly
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005
Compressive Hyperspectral Microscopy
Abstract
Compressive sensing (CS) is a novel imaging technology
based on the inherent redundancy within most natural
scenes. As the microscopic images of most nanomaterials may be considered sparse under certain
transformations, it can take five to twenty times fewer
measurements to acquire an image, and at the same time,
achieve much higher sensitivity ratio compared to its
raster-scanning counterparts.
By applying CS in the 3-dimensional hyperspectral data
acquisition processes, we develop a hyperspectral
microscope system for the analysis of surface plasmon
scattering spectra of metal nanostructures. A CS-based
sum frequency generation imaging microscope is also
demonstrated with the gold pattern sample illuminated by
visible beam and infrared beam simultaneously. The
possibility of applying a structurally simple and more
economical scanning-mask instead of the digital
micromirror device is also explored.
Hyperspectral imaging in optical microscopy
is of great importance in the study of
various micro/nano-scale physical and
chemical phenomena. By investigating the
response of a specimen to electromagnetic
waves as a function of both spatial position
and wavelength, rich amount of information
can be achieved for the specimen, e.g. the
plasmonic scattering spectrum of each
metal nano-structure within the field of view,
or the fluorescence spectrum of each
fluorescent molecule in a biomedical Traditional ways to acquire a hyperspectral data cube
sample. However, the use of hyperspectral
Hagen N, Kester RT, Gao L, Tkaczyk TS; Opt.
imaging is limited by its high-dimensional
Eng. 2012 Jun 13, 51(11).
data acquisition technique.
The hyperspectral data cube can be recovered
from much fewer measurements than the total
number of pixels. At the same time, an
enhancement in sensitivity is achieved, because
the spectrometer receives the sum of the light from
half of all the pixels in each measurement.
Examples of Image
Sparsifying Transformations
We constructed a hyperspectral microscope based
on the principle of compressive sensing using a
fiber optic spectrometer as the point detector. It can
be used to study the scattering spectra of various
metal nano-structures as well as many other kinds
of samples.
Introduction to Compressive Imaging
Ag nanowires
Au nanobelt
“Polyol Synthesis of Silver
Nanowires: An Extensive
Parametric Study,” Sahin Coskun,
Burcu Aksoy, and Husnu Emrah
Unalan Crystal Growth &
Design 2011 11 (11), 4963-4969
“A Tunable Plasmon
Resonance in Gold
Nanobelts,” Lindsey J. E.
Anderson, Courtney M.
Payne, Yu-Rong Zhen, Peter
Nordlander, and Jason H.
Hafner
Nano Letters 2011 11 (11),
5034-5037
Ag nanowire spectrum
DMD
CS Sum Frequency
Generation Microscopy
A new sum frequency generation imaging microscope
using compressive sensing has been developed for
surface studies. Pseudorandom patterns were applied to a
light modulator and reflected the sum frequency (SF)
signal generated from the sample into a photomultiplier
tube detector. The image of the sample was reconstructed
using sparsity preserving algorithms from the SF signal.
The results demonstrate the CS technique achieved 16
times the pixel density beyond the resolution where the
raster scan strategy lost its ability to image the sample
due to the dilution of the SF signal below the detection
limit of the detector.
Reconstructed 32x32 SFG images. Signal from Au stripes was
bright and signal from Si substrate was dark.
This work is done in collaboration with Steven Baldelli and Xiaojun Cai at UH
Mask-based Compressive Imaging
Limits of the DMD
 Tilting mirrors cause diffraction issues when used with coherent light source;
 Mirror size is not suitable for mid-infrared imaging.
With the appropriate choice of mask and scanning patterns, the
convolution of the shifted image creates similar compressive
encoding as the patterns on the DMD. This not only eliminates
the grating-like artifacts, it also allows for customizing the
modulator element size to appropriate spectral range.
Micromirrors
Au nanobelt spectrum
Sample imaged by CCD camera
Rice Single-Pixel Camera
Sparse signals can be recovered from far fewer incoherent linear measurements than the
full number of raster measurements using an optical modulator.
To perform the incoherent measurement, a spatial light modulator such as a digital
micromirror device (DMD) is used to modulate the intensities of the image pixels, and
lenses are used to summing the modulated light onto a point detector. The DMD from TI is
an array of 2D mirrors that can tilt plus and minus 12 degrees rapidly. The image can be
reconstructed very closely or exactly via the L1 norm minimization or total variation (TV)
minimization technique.
5 slices of the normalized 128X128X100 hyperspectral data cubes between 500 nm and 620 nm.
Resolution: 128X128; Spectral resolution: 1.2 nm; Compression ratio: 3:1; Integration time 300 ms.
Advantages of CS imaging scheme
 Using single-element detector
 Economical realization of complicated imaging system, e.g. infrared
imaging, hyperspectral imaging
 Compression during measurement leading to less amount of data
IEEE Signal Processing Magazine, 25(2), pp. 83 - 91, March 2008
The
reconstructed
spectra
clearly show a blue shift of the
surface plasmon resonance
peak along the Au nanobelt,
which corresponds to the
changing cross-sectional aspect
ratio of the nanobelt.
We can build on the ideas of CS multi-scale video
(CS-MUVI) by creating a dual-scale mask pattern
that when scanning results in both coarse and fine
resolutions. The coarse resolution provides the
preview necessary for the optical flow calculation
needed to perform the full-scale L1 compressive
reconstruction.
Results for 5%, 10%, 20% and 50%
compression on a test image seen on the
right.
4-D microscopic system can be realized by applying CS-MUVI imaging framework to the
compressive hyperspectral microscope. With a temporal dimension added, hyperspectral
videos of living cells can be captured.
Original Image
This work is done in collaboration with Lindsey Anderson and Jason Hafner at Rice
Picture and schematic diagram
of SFG-CS microscope setup
32x32 Preview
L1 Reconstruction
This work is done in collaboration with Wotao Yin (Rice/UCLA)
An example of dualscale mask pattern