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Full-Text Articles in Signal Processing
Gradient-Based Edge Detection Using Nonlinear Edge-Enhancing Prefilters, Russell Hardie, Charles Boncelet
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
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
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 …
Spectral Band Selection And Classifier Design For A Multispectral Imaging Laser Radar, Russell Hardie, Mohan Vaidyanathan, Paul Mcmanamon
Spectral Band Selection And Classifier Design For A Multispectral Imaging Laser Radar, Russell Hardie, Mohan Vaidyanathan, Paul Mcmanamon
Russell C. Hardie
A statistical spectral band selection procedure and classifiers for an active multispectral laser radar (LADAR) sensor are described. The sensor will operate in the 1 to 5 mm wavelength region. The algorithms proposed are tested using library reflectance spectra for some representative background materials. The material classes considered include both natural (vegetation and soil) and man-made (camouflage cloth and tar-asphalt). The analysis includes noise statistics due to Gaussian receiver noise and target induced speckle variations in the LADAR return signal intensity. The results of this analysis are then directly applied to an artificially generated spatial template of a scene consisting …
Hybrid Order Statistic Filter And Its Application To Image Restoration, Elizabeth Thompson, Russell Hardie, Kenneth Barner
Hybrid Order Statistic Filter And Its Application To Image Restoration, Elizabeth Thompson, Russell Hardie, Kenneth Barner
Russell C. Hardie
We introduce a new nonlinear filter for signal and image restoration, the hybrid order statistic (HOS) filter. Because it exploits both rank- and spatial-order information, the HOS realizes the advantages of nonlinear filters in edge preservation and reduction of impulsive noise components while retaining the ability of the linear filter to suppress Gaussian noise. We show that the HOS filter exhibits improved performance over both the linear Wiener and the nonlinear L filters in reducing mean-squared error in the presence of contaminated Gaussian noise. In many cases it also performs favorably compared with the Ll and rank-conditioned rank selection filters.
Application Of Multi-Frame High-Resolution Image Reconstruction To Digital Microscopy, Frank Baxley, Russell Hardie
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
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, …
Scene-Based Nonuniformity Correction With Video Sequences And Registration, Russell Hardie, Majeed Hayat, Ernest Armstrong, Brian Yasuda
Scene-Based Nonuniformity Correction With Video Sequences And Registration, Russell Hardie, Majeed Hayat, Ernest Armstrong, Brian Yasuda
Russell C. Hardie
We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance ~or true scene value!. These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and …
A Post-Processing Technique For Extending Depth Of Focus In Conventional Optical Microscopy, Taufiq Widjanarko, Russell Hardie
A Post-Processing Technique For Extending Depth Of Focus In Conventional Optical Microscopy, Taufiq Widjanarko, Russell Hardie
Russell C. Hardie
In this paper, we propose a post-processing technique to obtain optical microscope images with extended depth of focus using a conventional microscope. With the proposed technique, we collect a sequence of images focused at different depths. We then combine the in-focus regions of each acquired frame to compose a single all-in-focus image. That is, a new image with extended depth of focus is obtained. The key to such an algorithm is in selecting the “in-focus” regions from each frame. In this paper, we describe the technique used to identify the in-focus region on every depth slice. Quantitative simulation results are …
Application Of The Stochastic Mixing Model To Hyperspectral Resolution Enhancement, Michael Eismann, Russell Hardie
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 …
An Algebraic Algorithm For Nonuniformity Correction In Focal-Plane Arrays, Bradley Ratliff, Majeed Hayat, Russell Hardie
An Algebraic Algorithm For Nonuniformity Correction In Focal-Plane Arrays, Bradley Ratliff, Majeed Hayat, Russell Hardie
Russell C. Hardie
A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity …
Subspace Partition Weighted Sum Filters For Image Restoration, Yong Lin, Russell Hardie, Kenneth Barner
Subspace Partition Weighted Sum Filters For Image Restoration, Yong Lin, Russell Hardie, Kenneth Barner
Russell C. Hardie
The previously proposed partition-based weighted sum (PWS) filters combine vector quantization (VQ) and linear finite impulse response (FIR) Wiener filtering concepts. By partitioning the observation space and applying a tuned Wiener filter to each partition, the PWS is spatially adaptive and has been shown to perform well in noise reduction applications. In this letter, we propose the subspace PWS (SPWS) filter and evaluate the efficacy of the SPWS filter in image deconvolution and noise reduction applications. In the SPWS filter, we project the observation vectors into a subspace using principal component analysis (PCA), or other methods, prior to partitioning. This …
Hyperspectral Resolution Enhancement Using High-Resolution Multispectral Imagery With Arbitrary Response Functions, Michael Eismann, Russell Hardie
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
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.