Paper
22 May 2002 Blurred image restoration using the type of blur and blur parameter identification on the neural network
Igor N. Aizenberg, Constantine Butakoff, Viktor N. Karnaukhov, Nikolay S. Merzlyakov, Olga Milukova
Author Affiliations +
Proceedings Volume 4667, Image Processing: Algorithms and Systems; (2002) https://doi.org/10.1117/12.468009
Event: Electronic Imaging, 2002, San Jose, California, United States
Abstract
As a rule, blur is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process. It can be caused by relative motion between the camera and the original scene, or by an optical system that is out of focus. Today there are different techniques available for solving of the restoration problem including Fourier domain techniques, regularization methods, recursive and iterative filters to name a few. But without knowing at least approximate parameters of the blur, these filters show poor results. If incorrect blur model is chosen then the image will be rather distorted much more than restored. The original solution of the blur and blur parameters identification problem is presented in this paper. A neural network based on multi-valued neurons is used for the blur and blur parameters identification. It is shown that using simple single-layered neural network it is possible to identify the type of the distorting operator. Four types of blur are considered: defocus, rectangular, motion and Gaussian ones. The parameters of the corresponding operator are identified using a similar neural network. After a type of blur and its parameters identification the image can be restored using several kinds of methods. Some fundamentals of image restoration are also considered.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Igor N. Aizenberg, Constantine Butakoff, Viktor N. Karnaukhov, Nikolay S. Merzlyakov, and Olga Milukova "Blurred image restoration using the type of blur and blur parameter identification on the neural network", Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); https://doi.org/10.1117/12.468009
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Cited by 27 scholarly publications.
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KEYWORDS
Image restoration

Neural networks

Digital filtering

Image processing

Lithium

Cameras

Image acquisition

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