This PDF file contains the front matter associated with SPIE Proceedings Volume 9870, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.
We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.
Compressive spectral imaging (CSI) captures coded and dispersed projections of the spatio-spectral source rather than direct measurements of the voxels. Using the coded projections, an l1 minimization reconstruction algorithm is then used to reconstruct the underlying scene. An architecture known as the snapshot colored compressive spectral imager (SCCSI) exploits the compression capabilities of CSI techniques and efficiently senses a spectral image using a single snapshot by means of a colored mosaic FPA detector and a dispersive element. In CSI, different coding patterns are used to acquire multiple snapshots, yielding improved reconstructions of spatially detailed and spectrally rich scenes. SCCSI however, does not admit multiple coding patterns since the pixelated tiling of optical filters is directly attached to the detector. This paper extends the concept of SCCSI to a system admitting multiple measurement shots by rotating the dispersive element such that the dispersed spatio-spectral source is coded and integrated at different detector pixels in each rotation. This approach allows the acquisition of a different set of coded projections on each measurement shot. Simulations show that increasing the number of measurement snapshots results on improved reconstructions. More specifically, a gain up to 7 dB is obtained when results from four measurement shots are compared to the reconstruction from a single SCCSI snapshot.
The theory of compressive sensing (CS) has opened up new opportunities in the field of imaging. However, its
implementation in this field is often not straight-forward and the optical imaging system engineer encounters
several hurdles on the way of compressive imaging (CI) realization. The principles of CI design may differ
drastically from the principles used for conventional imaging. Analytical tools developed for conventional imaging
may not be optimal for compressive imaging. Nor are the conventional imaging components. Therefore often the CI
designer needs to develop new tools, and imaging schemes. In this paper we overview the main challenges that
might arise in the design of compressive imaging systems. The challenges are demonstrated through four tasks and
systems: compressive two dimensional (2D) imager, compressive motion detection, compressive spectral imaging
and compressive holography.
The accuracy of InSAR DEMs is affected by the temporal decorrelation of SAR images which is due to atmosphere, land
use/cover, soil moisture, and roughness changes. Elimination of the temporal decorrelation of the master and slave image
improves the DEMs accuracy. In this study, the Independent Component Analysis was applied before interferometric
process. It was observed that using three ICA entries, ICA independent sources can be interpreted as background and
changed images. ICA when performed on the master and slave images using the same couple of additional images
produces two background images which enable the production of high quality DEMs. However, limitations exist in the
We present a scalable information-optimal compressive imager optimized for the target classification task, discriminating between two target classes. Compressive projections are optimized using the Cauchy-Schwarz Mutual Information (CSMI) metric, which provides an upper-bound to the probability of error of target classification. The optimized measurements provide significant performance improvement relative to random and PCA secant projections. We validate the simulation performance of information-optimal compressive measurements with experimental data.
In this paper, we address the problem of accelerating inversion algorithms for nonlinear acoustic tomographic imaging by parallel computing on graphics processing units (GPUs). Nonlinear inversion algorithms for tomographic imaging often rely on iterative algorithms for solving an inverse problem, thus computationally intensive. We study the simultaneous iterative reconstruction technique (SIRT) for the multiple-input-multiple-output (MIMO) tomography algorithm which enables parallel computations of the grid points as well as the parallel execution of multiple source excitation. Using graphics processing units (GPUs) and the Compute Uniﬁed Device Architecture (CUDA) programming model an overall improvement of 26.33x was achieved when combining both approaches compared with sequential algorithms. Furthermore we propose an adaptive iterative relaxation factor and the use of non-uniform weights to improve the overall convergence of the algorithm. Using these techniques, fast computations can be performed in parallel without the loss of image quality during the reconstruction process.
The transport-of-intensity equation (TIE) is often used to determine the phase and amplitude profile of a complex object by monitoring the intensities at different distances of propagation or around the image plane. TIE results from the imaginary part of the paraxial wave equation and is equivalent to the conservation of energy. The real part of the paraxial wave equation gives the eikonal equation in the presence of diffraction. Since propagation of the optical field between different planes is governed by the (paraxial) wave equation, both real and imaginary parts need to be satisfied at every propagation plane. In this work, the solution of the TIE is optimized by using the real part of the paraxial wave equation as a constraint. This technique is applied to the more exact determination of imaging the induced phase of a liquid heated by a focused laser beam, which has been previously computed using TIE only. Retrieval of imaged phase using the TIE is performed by using the constraint that naturally arises from the real part of the paraxial wave equation.
We present a three-dimensional microscopic technique based on digital holographic imaging, which allows highly accurate axial localization of features inside of a three dimensional sample. When a light wave is propagating through, or reflecting from, a microscopic object, the phase changes can be converted into intensity variations using the existing digital microscopic techniques. The phase change indicates the change in the optical path length, which can be then converted to physical thickness, providing the sample height information. This property of holograms is used in phasecontrast techniques, and can also be used for quantitative 3D imaging. However, if the sample contains features with different indices of refraction, this method can only provide the overall optical thickness, and cannot determine where in the axial direction the particular feature is located. As a result, the application of Digital Holographic Microscopy to imaging of organelles within live cells, or defects within semiconductor substrates, is limited to overall morphology of the sample. To determine the axial location of features inside of a three dimensional sample, we developed a phase image processing method based on analyzing images taken from non-zero incident angles. When compared, these images can discriminate between various axial depths of features, while still retaining the information about the overall thickness profile of the sample.
Propagation-based phase contrast using the transport of intensity equation (TIE) allows rapid, deterministic phase retrieval from defocused images. For weakly attenuating objects, phase can be retrieved from a single image. However, the TIE suﬀers from significant low frequency artifacts due to enhancement of noise during phase retrieval. We demonstrate that by patterning the illumination source as approximately a modified Bessel function of the 2nd kind of zero order, quantitative phase can be imaged directly at the detector within a spatial frequency band. Outside of that band, Bessel sources still improve low frequency performance in phase retrieval.
In this paper, we demonstrate simple algorithms that project low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithms are very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. are used in projection. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML) algorithms. The algorithms are robust and are not overly sensitive to the registration inaccuracies.
Possession of a working 3D printed key can, for most practical purposes, convince observers that an illicit attempt to gain premises access is authorized. This paper seeks to assess three things. First, work has been performed to determine how easily the data for making models of keys can be obtained through manual measurement. It then presents work done to create a model of the key and determine how easy key modeling could be (particularly after a first key of a given key ‘blank’ has been made). Finally, it seeks to assess the durability of the keys produced using 3D printing.
Recently we have been concerned with locating and tracking images of fish in underwater videos. While edge detection and region growing have assisted in obtaining some advances in this effort, a more extensive, non-linear approach appears necessary for improved results. In particular, the use of particle filtering applied to contour detection in natural images has met with some success. Following recent ideas in the literature, we are proposing to use a recursive Bayesian model which employs a sequential Monte Carlo approach, also known as the particle filter. This approach uses the corroboration between two scales of an image to produce various local features which characterize the different probability densities required by the particle filter. Since our data consist of video images of fish recorded by a stationary camera, we are capable of augmenting this process by means of background subtraction. Moreover, we are proposing a method that does not require the pre-computation of the distributions required by the particle filter. The above capabilities are applied to our dataset for the purpose of using contour detection with the aim of eventual segmentation of the fish images and fish classification. Although our dataset consists of fish images, the proposed techniques can be employed in applications involving different kinds of non-stationary underwater objects. We present results and examples of this analysis and discuss the particle filter application to our dataset.
In this paper we propose a stitching algorithm of medical images into one. The algorithm is designed to stitching the medical x-ray imaging, biological particles in microscopic images, medical microscopic images and other. Such image can improve the diagnosis accuracy and quality for minimally invasive studies (e.g., laparoscopy, ophthalmology and other). The proposed algorithm is based on the following steps: the searching and selection areas with overlap boundaries; the keypoint and feature detection; the preliminary stitching images and transformation to reduce the visible distortion; the search a single unified borders in overlap area; brightness, contrast and white balance converting; the superimposition into a one image. Experimental results demonstrate the effectiveness of the proposed method in the task of image stitching.