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Signal Processing and Performance Analysis for Imaging Systems

Signal Processing and Performance Analysis for Imaging Systems

Copyright: 2008
Pages: 270
ISBN: 9781596932876

Print Book $39.00 Qty:
eBook $164.00 Qty:
Hone your image enhancement skills to their sharpest with this practical and comprehensive resource. It presents today's most powerful signal processing techniques together with methods for assessing imaging system performance when each of these techniques is applied. This multi-use book helps you make the most of sensor hardware through software enhancement, and evaluate system and algorithm performance. You also learn how to make the best hardware/software decisions in developing the next-generation of image acquisition and analysis systems. From image resampling to image fusion, this hands-on book brings a wealth of cutting-edge signal and image processing tools into focus. You get step-by-step details on a super-resolution image reconstruction methods that produces superior high-resolution images from low-cost, low-resolution video. You discover an image deblur/restoration technique that works even in the presence of high noise. The book includes contrast enhancement tools for generating the highest-contrast images in low-light situations, as well as non-uniformity correction (NUC) methods that most effectively reduce fixed-pattern noise from the imaging sensor. What's more, the book presents a special tone scale technique that creates the best image presentation whether on-screen or in print. You also find powerful image fusion techniques for improving such tasks as target recognition and identification. This well-illustrated book is supported with nearly 350 time-saving equations.
Part 1 IntroductionCombined Imaging System Performance. Imaging Performance. Signal Processing: Basic Principles and Advanced Applications. Image Resampling. Super-resolution Image Reconstruction. Deblur Filtering. Image Contrast Enhancement. Non-Uniformity Correction. Tone Scale. Image Fusion. ; Imaging SystemsThe Basic Imaging System. Resolution and Sensitivity. Linear Shift Invariant Imaging Systems. Imaging System Point Spread Function (PSF) and Modulation Transfer Function (MTF). Sampled Imaging System. Signal to Noise Ratio. Electro-Optical and Infrared Imaging Systems. ; Target Acquisition and Image QualityIntroduction. A Brief History of Target Acquisition Theory. Threshold Vision. Image Quality Metric. Example. ; Part 2 ; Basic Principles of Signal and Image ProcessingIntroduction. The Fourier Transform. Finite Impulse Response (FIR) Filters. Fourier-based Filters. The Wavelet Transform. Summary. ; Part 3: Advanced Applications ; Image Resampling Introduction. Image Display, Reconstruction, and Resampling. Sampling Theory and Sampling Artifacts. Image Resampling using Spatial Domain Methods. Alias-free Image Resampling using Fourier-based Methods. Image Resampling Performance Measurements. ; Super-Resolution Image ReconstructionIntroduction. Super-Resolution Image Restoration. Super-Resolution Image Reconstruction. Super-Resolution Image Performance Measurements. Sensors that Benefit from Super-Resolution Reconstruction. Performance Modeling and Prediction of Super-Resolution Reconstruction. ; Deblur FilteringIntroduction. Regularization Methods. Wiener Filter. Van Cittert Filter. CLEAN Algorithm. P-deblurring Filter. Deblur Filtering Image Performance Measurements. ; Image Contrast EnhancementIntroduction. Single Scale Process. Multiscale Processing. Contrast Enhancement Image Performance Measurements. ; Non-Uniformity CorrectionDetector Non-Uniformity. Linear Correction and the Effects of Non-linearity. Adaptive Non-Uniformity Correction. Imaging System Performance with Fixed-Pattern Noise. ; Tone ScaleIntroduction. Piece-Wise Linear Tone Scale. Non-linear Tone Scale. Perceptual Linearization Tone Scale. Application of Tone Scale to Enhanced Visualization in Radiation Treatment. Tone Scale Performance Example. ; Image FusionIntroduction. Objectives for Image Fusion. Image Fusion Algorithms. Benefits of Multiple Image Modes. Image Fusion Quality Metrics. Imaging System Performance with Image Fusion.;
  • Ronald G. Driggers Ronald Driggers is the superintendent in the Optical Sciences Division of the U.S. Naval Research Laboratory. He was previously a senior engineer at U.S. Army Night Vision and Electronic Sensors Directorate where he provided electro-optical and infrared research on performance modeling. Dr. Driggers received his Ph.D., M.S., and B.S. from the University of Memphis.
  • Eddie L. Jacobs Eddie L. Jacobs is an assistant professor of electrical engineering at the University of Memphis and former branch chief of the Sensor Performance Modeling team at the U.S. Army Night Vision and Electronic Sensors Directorate. He received his D.Sc in electrophysics from The George Washington University.
  • S. Susan Young S. Susan Young is a research scientist with the U.S. Army Research Laboratory. Previously, she was a Research Associate at the Department of Radiation Medicine at Roswell Park Cancer Institute. Later, she became a Senior Research Scientist at Health Imaging Research Laboratory, Eastman Kodak Company. She received her Ph.D. in electrical engineering at SUNY Buffalo. She has published over 50 papers in reputable journals and conferences. She holds six patents for inventions that are related to medical diagnostic imaging, image compression, image enhancement, pattern analysis, and image super-resolution. She is a recipient of a 2007 U.S. Army Research and Development Achievement Award.
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