16 September 1994 Super-exponential method for blur identification and image restoration
Author Affiliations +
Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994); doi: 10.1117/12.186036
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States
This paper examines a super-exponential method for blind deconvolution. Possibly non- minimal phase point spread functions (PSFs) are identified. The PSF is assumed to be low pass in nature. No other prior knowledge of the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown using synthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experiments suggest a promising application of this algorithm to a variety of blur identification problems.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas J. Kostas, Laurent M. Mugnier, Aggelos K. Katsaggelos, Alan V. Sahakian, "Super-exponential method for blur identification and image restoration", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.186036; https://doi.org/10.1117/12.186036

Point spread functions

Image restoration


Image segmentation

X-ray imaging

Autoregressive models

Satellite imaging


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