Electromagnetics and Photonics Commons^™

Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore

Russell C. Hardie

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a …

Analysis Of Various Classification Techniques For Computer Aided Detection System Of Pulmonary Nodules In Ct, Barath Narayanan Narayanan, Russell C. Hardie, Temesguen Messay

Russell C. Hardie

Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules. Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include (i) local contrast enhancement (ii) …

On The Simulation And Mitigation Of Anisoplanatic Optical Turbulence For Long Range Imaging, Russell C. Hardie, Daniel A. Lemaster

Russell C. Hardie

We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an …

Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie

Russell C. Hardie

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video …

Block Matching And Wiener Filtering Approach To Optical Turbulence Mitigation And Its Application To Simulated And Real Imagery With Quantitative Error Analysis, Russell C. Hardie, Michael Armand Rucci, Barry K. Karch, Alexander J. Dapore

Russell C. Hardie

We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames …

Simulation Of Anisoplanatic Imaging Through Optical Turbulence Using Numerical Wave Propagation With New Validation Analysis, Russell C. Hardie, Jonathan D. Power, Daniel A. Lemaster, Douglas R. Droege, Szymon Gladysz, Santasri Bose-Pillai

Russell C. Hardie

We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation …

Differential Tilt Variance Effects Of Turbulence In Imagery: Comparing Simulation With Theory, Daniel A. Lemaster, Russell C. Hardie, Szymon Gladysz, Matthew D. Howard, Michael Armand Rucci, Matthew E. Trippel, Jonathan D. Power, Barry K. Karch

Russell C. Hardie

Differential tilt variance is a useful metric for interpreting the distorting effects of turbulence in incoherent imaging systems. In this paper, we compare the theoretical model of differential tilt variance to simulations. Simulation is based on a Monte Carlo wave optics approach with split step propagation. Results show that the simulation closely matches theory. The results also show that care must be taken when selecting a method to estimate tilts.

Digital Image Processing, Russell C. Hardie, Majeed M. Hayat

Russell C. Hardie

In recent years, digital images and digital image processing have become part of everyday life. This growth has been primarily fueled by advances in digital computers and the advent and growth of the Internet. Furthermore, commercially available digital cameras, scanners, and other equipment for acquiring, storing, and displaying digital imagery have become very inexpensive and increasingly powerful. An excellent treatment of digital images and digital image processing can be found in Ref. [1]. A digital image is simply a two-dimensional array of finite-precision numerical values called picture elements (or pixels). Thus a digital image is a spatially discrete (or discrete-space) …

Segmentation Of Pulmonary Nodules In Computed Tomography Using A Regression Neural Network Approach And Its Application To The Lung Image Database Consortium And Image Database Resource Initiative Dataset, Temesguen Messay, Russell C. Hardie, Timothy R. Tuinstra

Russell C. Hardie

We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system.

If improved segmentation results are needed, the SA …

Spatial-Rank Order Selection Filters, Kenneth Barner, Russell Hardie

Russell C. Hardie

Chapter 3: "Spatial-Rank Order Selection Filters"
3.1 Introduction
3.2 Selection Filters and Spatial-Rank Ordering
3.3 Spatial-Rank Order Selection Filters
3.4 Optimization
3.5 Applications
3.6 Future Directions

Gradient-Based Edge Detection Using Nonlinear Edge-Enhancing Prefilters, Russell Hardie, Charles Boncelet

Russell C. Hardie

This correspondence examines the use of nonlinear edge enhancers as prefilters for edge detectors. The filters are able to convert smooth edges to step edges and suppress noise simultaneously. Thus, false alarms due to noise are minimized and edge gradient estimates tend to be large and localized. This leads to significantly improved edge maps.

Ranking In Rp And Its Use In Multivariate Image Estimation, Russell Hardie, Gonzalo Arce

Russell C. Hardie

The extension of ranking a set of elements in R to ranking a set of vectors in a p'th dimensional space Rp is considered. In the approach presented here vector ranking reduces to ordering vectors according to a sorted list of vector distances. A statistical analysis of this vector ranking is presented, and these vector ranking concepts are then used to develop ranked-order type estimators for multivariate image fields. A class of vector filters is developed, which are efficient smoothers in additive noise and can be designed to have detail-preserving characteristics. A statistical analysis is developed for the class of …

Lum Filters: A Class Of Rank-Order-Based Filters For Smoothing And Sharpening, Russell Hardie, Charles Boncelet

Russell C. Hardie

A new class of rank-order-based filters, called lower-upper-middle (LUM) filters, is introduced. The output of these filters is determined by comparing a lower- and an upper-order statistic to the middle sample in the filter window. These filters can be designed for smoothing and sharpening, or outlier rejection. The level of smoothing done by the filter can range from no smoothing to that of the medianfilter. This flexibility allows the LUM filter to be designed to best balance the tradeoffs between noisesmoothing and signal detail preservation. LUM filters for enhancing edge gradients can be designed to be insensitive to low levels …

Infrared Image Registration And High-Resolution Reconstruction Using Multiple Translationally Shifted Aliased Video Frames, Mohammad Alam, John Bognar, Russell Hardie, Brian Yasuda

Russell C. Hardie

Forward looking infrared (FLIR) detector arrays generally produce spatially undersampled images because the FLIR arrays cannot be made dense enough to yield a sufficiently high spatial sampling frequency. Multi-frame techniques, such as microscanning, are an effective means of reducing aliasing and increasing resolution in images produced by staring imaging systems. These techniques involve interlacing a set of image frames that have been shifted with respect to each other during acquisition. The FLIR system is mounted on a moving platform, such as an aircraft, and the vibrations associated with the platform are used to generate the shifts. Since a fixed number …

Application Of Multi-Frame High-Resolution Image Reconstruction To Digital Microscopy, Frank Baxley, Russell Hardie

Russell C. Hardie

A high-resolution image reconstruction algorithm previously used to improve undersampled infrared airborne imagery was applied to two different sets of digital microscopy images. One set is that of medical pap smear images, and the second set contains metallurgical micrographs. Both the pap smear images and the metallurgical micrographs are undersampled, thus causing loss of detail and aliasing artifacts. The algorithm minimizes the effects of aliasing and restores detail unobtainable through simple interpolation techniques. Both applications demonstrate improvement by use of the image reconstruction algorithm.

Robust Phase-Unwrapping Algorithm Using A Spatial Binary-Tree Image Decomposition, Russell Hardie, Md. Younus, James Blackshire

Russell C. Hardie

The search for fast and robust phase-unwrapping algorithms remains an important problem in the development of real-time interferometric systems. Our phase-unwrapping approach uses a spatial binary-tree image decomposition to permit maximum parallelism in implementation. At each node in the tree structure, a single unwrapping decision is made between two image blocks. The unwrapping rule is derived from a statistical-estimation framework. Specifically, a maximum-likelihood estimate of the demodulation term is used. This term can be viewed as that which minimizes a discontinuity-penalizing cost function. We show that the algorithm exhibits a high level of robustness. Quantitative measures of performance are provided, …

Application Of The Stochastic Mixing Model To Hyperspectral Resolution Enhancement, Michael Eismann, Russell Hardie

Russell C. Hardie

A maximum a posteriori (MAP) estimation method is described for enhancing the spatial resolution of a hyperspectral image using a higher resolution coincident panchromatic image. The approach makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to develop a cost function that simultaneously optimizes the estimated hyperspectral scene relative to the observed hyperspectral and panchromatic imagery, as well as the local statistics of the spectral mixing model. The incorporation of the stochastic mixing model is found to be the key ingredient for reconstructing subpixel spectral information in that it provides the necessary constraints that lead …

Performance Analysis Of A New Computer Aided Detection System For Identifying Lung Nodules On Chest Radiographs, Russell Hardie, Steven Rogers, Terry Wilson, Adam Rogers

Russell C. Hardie

A new computer aided detection (CAD) system is presented for the detection of pulmonary nodules on chest radiographs. Here, we present the details of the proposed algorithm and provide a performance analysis using a publicly available database to serve as a benchmark for future research efforts. All aspects of algorithm training were done using an independent dataset containing 167 chest radiographs with a total of 181 lung nodules. The publicly available test set was created by the Standard Digital Image Database Project Team of the Scientific Committee of the Japanese Society of Radiological Technology (JRST). The JRST dataset used here …

Stochastic Spectral Unmixing With Enhanced Endmember Class Separation, Michael Eismann, Russell Hardie

Russell C. Hardie

Improvements to an algorithm for performing spectral unmixing of hyperspectral imagery based on the stochastic mixing model (SMM) are presented. The SMM provides a method for characterizing both subpixel mixing of the pure image constituents, or endmembers, and statistical variation in the endmember spectra that is due, for example, to sensor noise and natural variability of the pure constituents. Modifications of the iterative, expectation maximization approach to deriving the SMM parameter estimates are proposed, and their effects on unmixing performance are characterized. These modifications specifically concern algorithm initialization, random class assignment, and mixture constraints. The results show that the enhanced …

Hyperspectral Resolution Enhancement Using High-Resolution Multispectral Imagery With Arbitrary Response Functions, Michael Eismann, Russell Hardie

Russell C. Hardie

A maximum a posteriori (MAP) estimation method for improving the spatial resolution of a hyperspectral image using a higher resolution auxiliary image is extended to address several practical remote sensing situations. These include cases where: 1) the spectral response of the auxiliary image is unknown and does not match that of the hyperspectral image; 2) the auxiliary image is multispectral; and 3) the spatial point spread function for the hyperspectral sensor is arbitrary and extends beyond the span of the detector elements. The research presented follows a previously reported MAP approach that makes use of a stochastic mixing model (SMM) …

Improved Optimization Of Soft Partition Weighted Sum Filters And Their Application To Image Restoration, Yong Lin, Russell Hardie, Qin Sheng, Kenneth Barner

Russell C. Hardie

Soft-partition-weighted-sum (Soft-PWS) filters are a class of spatially adaptive moving-window filters for signal and image restoration. Their performance is shown to be promising. However, optimization of the Soft-PWS filters has received only limited attention. Earlier work focused on a stochastic-gradient method that is computationally prohibitive in many applications. We describe a novel radial basis function interpretation of the Soft-PWS filters and present an efficient optimization procedure. We apply the filters to the problem of noise reduction. The experimental results show that the Soft-PWS filter outperforms the standard partition-weighted-sum filter and the Wiener filter.

A Computationally Efficient Super-Resolution Algorithm For Video Processing Using Partition Filters, Balaji Narayanan, Russell Hardie, Kenneth Barner, Min Shao

Russell C. Hardie

We propose a computationally efficient super-resolution (SR) algorithm to produce high-resolution videofrom low-resolution (LR) video using partition-based weighted sum (PWS) filters. First, subpixel motion parameters are estimated from the LR video frames. These are used to position the observed LR pixels into a high-resolution (HR) grid. Finally, PWS filters are employed to simultaneously perform nonuniform interpolation (to fully populate the HR grid) and perform deconvolution of the system point spread function. The PWS filters operate with a moving window. At each window location, the output is formedusing a weighted sum of the present pixels within the window. The weights are …

Adaptive Wiener Filter Super-Resolution Of Color Filter Array Images, Barry K. Karch, Russell C. Hardie

Russell C. Hardie

Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method …

Joint Map Registration And High Resolution Image Estimation Using A Sequence Of Undersampled Images, Russell C. Hardie, Kenneth J. Barnard, Ernest E. Armstrong

Russell C. Hardie

n many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the high-resolution image is presented. Several previous approaches have relied on knowing the registration parameters …