Signal Processing and Performance Analysis for Imaging Systems

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Overview

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.

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Product Details

  • ISBN-13: 9781596932876
  • Publisher: Artech House, Incorporated
  • Publication date: 4/28/2008
  • Pages: 304
  • Product dimensions: 7.20 (w) x 10.10 (h) x 0.90 (d)

Meet the Author

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.

Ronald G. Driggers is director of the Modeling and Simulation Division in the U.S. Army Night Vision & Electronic Sensors Directorate. He is the author or co-author of several books in the field including Introduction to Infrared and Electro-Optical Systems and Surveillance and Reconnaissance Imaging Systems (Artech House, 1999, 2001), editor-in-chief of the Encyclopedia of Optical Engineering, and associate editor of Applied Optics. He earned his Ph.D. in electrical engineering from the University of Memphis.

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.

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Table of Contents

Part 1
Introduction—Combined 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 Systems—The 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 Quality—Introduction. A Brief History of Target Acquisition Theory. Threshold Vision. Image Quality Metric. Example.

Part 2
Basic Principles of Signal and Image Processing—Introduction. 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 Reconstruction—Introduction. 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 Filtering—Introduction. Regularization Methods. Wiener Filter. Van Cittert Filter. CLEAN Algorithm. P-deblurring Filter. Deblur Filtering Image Performance Measurements.

Image Contrast Enhancement—Introduction. Single Scale Process. Multiscale Processing. Contrast Enhancement Image Performance Measurements.

Non-Uniformity Correction—Detector Non-Uniformity. Linear Correction and the Effects of Non-linearity. Adaptive Non-Uniformity Correction. Imaging System Performance with Fixed-Pattern Noise.

Tone Scale—Introduction. 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 Fusion— Introduction. Objectives for Image Fusion. Image Fusion Algorithms. Benefits of Multiple Image Modes. Image Fusion Quality Metrics. Imaging System Performance with Image Fusion.

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