Publisher's Synopsis
Chapter 1, Image Restoration on the Hopfield Neural Network, covers image restoration algorithms originally aimed at achieving performance beyond the diffraction limit, but which are in fact capable of compensating simultaneously or separately for aberrations induced by the optical components and for the limitations of the detector. Basic properties of the Hopfield Network are described along with a demonstration of how it provides a framework for interpreting a variety of opt@ation procedures useful in image restoration.;The image restoration problem considered is that of improving the resolution of a low pass filtered image or retrieving a high resolution image from limited noisy and/or distorted spectral data.;The second chapter, Fundamentals and Special Applications of Non-Contact Scanning Force Microscopy, reviews the basic ftmdamentals and some of the important applications of non-contact mode force microscopy, including a detailed discussion of the various surface forces that may occur between the probe and the sample, an introduction to electronic force microscopy, and a section devoted to the basics of magnetic force microscopy currently the most important application of the force microscope. Some general principles of instrumentation are also discussed.;Chapter 3 is Electrical Noise as a Measure of Quality and Reliability in Electronic Devices. Most electronic devices and components produce random electrical signals or noise greater than the well established thermal and shot noise sources. This excess noise depends on the structural defects, impurities, and non-ideal properties of the device. Because noise is a very sensitive indicator of the presence of imperfections in the device, it can be used as a non-destructive measure of the quality, and less directly, the probable lifetime or reliabili ty of the device. An account is given of the particular mechanisms which are well established in individual devices along with a review of the success of the use of noise as a quality or reliability indicator.;The final chapter, Parallel Processing Methodologies for Image Processing and Computer Vision, discusses two predominant methodologies in the application of parallel processing technology to image processing and computer vision problems: architecture and application driven methodologies. These represent complementary approaches to the achievement of the same goal: high speed image processing and computer vision. Examples of parallel processing architectures evolved from both methodologies are discussed and compared. General properties of both approaches are presented with a view toward matching specific image processing and computer vision algorithms to appropriate methodologies.
