Digital image restoration
The field of digital image restoration is concerned with the reconstruction or estimation of uncorrupted images from noisy, blurred ones. This blurring may be caused by optical distortions, object motion during imaging, or atmospheric turbulence. There are existing or potential applications of image restoration in many scientific and engineering fields, e.g. aerial imaging, remote sensing electron microscopy, and medical imaging. This book describes recent advances and provides a survey of the field. New research results are presented on the formulation of the restoration problem, the implementation of restoration algorithms using artificial neural networks, the derivation and application of nonstationary mathematical image models, the development of simultaneous image and blur parameter identification and restoration algorithms, and the development of algorithms for restoring scanned photographic images. Special attention is paid to issues of numerical instrumentation. A large number of illustrations demonstrate the performance of the restoration approaches.
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HopfieldType Neural Networks
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A.K.Katsaggelos ACAM approximation assumed block circulant blur identification blur PSF blurred image circulant matrix complete data computation conditional convergence convex set corresponding covariance cross entropy defined degraded image denotes density domain deterministic dual approach equation exposure domain film Gaussian gray level Hopfield network IEEE IEEE Trans ill-posed problems image and blur image estimation image model Image Processing implementation iterations Kalman filter Katsaggelos Lagrange multiplier least-squares library vectors likelihood function linear MAP estimate Markov random field matrix maximum entropy maximum likelihood minimization minimum cross entropy minimum phase model parameters multiplicative noise neural network neuron noisy image nonlinear observation noise observed image obtained optimally restored original image parameter identification perceptron photographic images pixels power spectrum Proc recursive region restoration algorithms restoration results restored image restored signal ROMKF samples Sect shown in Fig signal model signals restored simulated annealing spatial stable stochastic techniques Tekalp update values variance Wiener filter zero