Image Processing using Punctual Kriging and Machine Learning
Applications of fuzzy logic, genetic programming and artificial neural networks in the field of Image Restoration
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Restoration of degraded images has become an important and effective tool for many technological applications like space imaging, medical imaging and many other post-processing techniques. Most of the image restoration techniques model the degradation phenomena, usually blur and noise, and then obtain an approximation of the image. Whereas, in realistic situation, one has to estimate both the true image and the blur from the degraded image characteristics in the absence of any a priori information about the blurring system. The objective of this book is to present new punctual kriging based im...
Restoration of degraded images has become an important and effective tool for many technological applications like space imaging, medical imaging and many other post-processing techniques. Most of the image restoration techniques model the degradation phenomena, usually blur and noise, and then obtain an approximation of the image. Whereas, in realistic situation, one has to estimate both the true image and the blur from the degraded image characteristics in the absence of any a priori information about the blurring system. The objective of this book is to present new punctual kriging based image restoration approaches using machine-learning techniques. To achieve this objective, this book concentrates on the restoration of images corrupted with Gaussian noise by making good tradeoffs between two contradicting properties; smoothness versus edge preservation. This book makes the following contributions: Quantitative analysis of the at hand punctual kriging based image restoration techniques, applications of artificial neural networks are discussed for image denoising. Furthermore, hybrid techniques for image restoration are presented in this book.
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