34,667 research outputs found
Logarithmic intensity and speckle-based motion contrast methods for human retinal vasculature visualization using swept source optical coherence tomography
We formulate a theory to show that the statistics of OCT signal amplitude and intensity are highly dependent on the sample reflectivity strength, motion, and noise power. Our theoretical and experimental results depict the lack of speckle amplitude and intensity contrasts to differentiate regions of motion from static areas. Two logarithmic intensity-based contrasts, logarithmic intensity variance (LOGIV) and differential logarithmic intensity variance (DLOGIV), are proposed for serving as surrogate markers for motion with enhanced sensitivity. Our findings demonstrate a good agreement between the theoretical and experimental results for logarithmic intensity-based contrasts. Logarithmic intensity-based motion and speckle-based contrast methods are validated and compared for in vivo human retinal vasculature visualization using high-speed swept-source optical coherence tomography (SS-OCT) at 1060 nm. The vasculature was identified as regions of motion by creating LOGIV and DLOGIV tomograms: multiple B-scans were collected of individual slices through the retina and the variance of logarithmic intensities and differences of logarithmic intensities were calculated. Both methods captured the small vessels and the meshwork of capillaries associated with the inner retina in en face images over 4 mm^2 in a normal subject
Computational illumination for high-speed in vitro Fourier ptychographic microscopy
We demonstrate a new computational illumination technique that achieves large
space-bandwidth-time product, for quantitative phase imaging of unstained live
samples in vitro. Microscope lenses can have either large field of view (FOV)
or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new
computational imaging technique that circumvents this limit by fusing
information from multiple images taken with different illumination angles. The
result is a gigapixel-scale image having both wide FOV and high resolution,
i.e. large space-bandwidth product (SBP). FPM has enormous potential for
revolutionizing microscopy and has already found application in digital
pathology. However, it suffers from long acquisition times (on the order of
minutes), limiting throughput. Faster capture times would not only improve
imaging speed, but also allow studies of live samples, where motion artifacts
degrade results. In contrast to fixed (e.g. pathology) slides, live samples are
continuously evolving at various spatial and temporal scales. Here, we present
a new source coding scheme, along with real-time hardware control, to achieve
0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an
improved algorithm and new initialization scheme, which allow robust phase
reconstruction over long time-lapse experiments. We present the first FPM
results for both growing and confluent in vitro cell cultures, capturing videos
of subcellular dynamical phenomena in popular cell lines undergoing division
and migration. Our method opens up FPM to applications with live samples, for
observing rare events in both space and time
Gemini Planet Imager Observational Calibrations III: Empirical Measurement Methods and Applications of High-Resolution Microlens PSFs
The newly commissioned Gemini Planet Imager (GPI) combines extreme adaptive
optics, an advanced coronagraph, precision wavefront control and a
lenslet-based integral field spectrograph (IFS) to measure the spectra of young
extrasolar giant planets between 0.9-2.5 um. Each GPI detector image, when in
spectral model, consists of ~37,000 microspectra which are under or critically
sampled in the spatial direction. This paper demonstrates how to obtain
high-resolution microlens PSFs and discusses their use in enhancing the
wavelength calibration, flexure compensation and spectral extraction. This
method is generally applicable to any lenslet-based integral field spectrograph
including proposed future instrument concepts for space missions.Comment: 10 pages, 6 figures. Proceedings of the SPIE, 9147-282 v2: reference
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Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Advanced video classification systems decode video frames to derive the
necessary texture and motion representations for ingestion and analysis by
spatio-temporal deep convolutional neural networks (CNNs). However, when
considering visual Internet-of-Things applications, surveillance systems and
semantic crawlers of large video repositories, the video capture and the
CNN-based semantic analysis parts do not tend to be co-located. This
necessitates the transport of compressed video over networks and incurs
significant overhead in bandwidth and energy consumption, thereby significantly
undermining the deployment potential of such systems. In this paper, we
investigate the trade-off between the encoding bitrate and the achievable
accuracy of CNN-based video classification models that directly ingest
AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video
bitstreams and applying complex optical flow calculations prior to CNN
processing, we only retain motion vector and select texture information at
significantly-reduced bitrates and apply no additional processing prior to CNN
ingestion. Based on three CNN architectures and two action recognition
datasets, we achieve 11%-94% saving in bitrate with marginal effect on
classification accuracy. A model-based selection between multiple CNNs
increases these savings further, to the point where, if up to 7% loss of
accuracy can be tolerated, video classification can take place with as little
as 3 kbps for the transport of the required compressed video information to the
system implementing the CNN models
Novel image processing algorithms and methods for improving their robustness and operational performance
Image processing algorithms have developed rapidly in recent years. Imaging functions are becoming more common in electronic devices, demanding better image quality, and more robust image capture in challenging conditions. Increasingly more complicated algorithms are being developed in order to achieve better signal to noise characteristics, more accurate colours, and wider dynamic range, in order to approach the human visual system performance levels. [Continues.
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