Theoretical Development of Performance Bounds for Image Restoration
As many image restoration techniques are continuing to be developed, it is increasingly difficult to compare the performance of various methods. Although some image-quality measures have been presented in the literature , it is inappropriate to choose a particular measure as a benchmark of performance evaluation for a wide range of applications. More importantly, none of these quality measures can be used as a performance bound which usually indicates how much potential performance can be improved for a specific restoration scheme. Therefore, it is extremely important to develop theoretical performance bounds under a variety of image and noise models for general image restoration schemes.
KeywordsOriginal Image Point Spread Function Fisher Information Noise Model Unbiased Estimator
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- 2.S. Zacks. The Theory of Statistical Inference. John Wiley & Sons. Inc.. 1971.Google Scholar
- 3.H. L. Van Trees, Detection. Estimation, and Modulation Theory. John Wiley & Sons. Inc.. 1968.Google Scholar
- 4.H. Hung. “The Coherent Signal-Subspace Approach to the Estimation of the Parameters of Multiple Wideband Sources”. Ph.D dissertation, University of Minnesota. Jan. 1988.Google Scholar
- 5.R. Kasturi and J. F. Walkup. “Nonlinear Image Restoration in Signal-dependent Noise”. Advances in Computer Vision and Image Processing. vol. 2. pp. 167–212. JAI Press Inc.. 1986.Google Scholar
- 6.G. K. Froehlich. J. F. Walkup. and R. B. Asher. “Optimal Estimation in Signal-dependent Noise”, J. Opt. Soc. Am.. Vol. 68, No. 12, Dec. 1978.Google Scholar