An empirical identification method of Gaussian blur parameter for image deblurring
IEEE Transactions on Signal Processing
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
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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.