1 October 1995 Model-based neural evaluation and iterative gradient optimization in image restoration and statistical filtering
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J. of Electronic Imaging, 4(4), (1995). doi:10.1117/12.217268
Abstract
An optimal model-based neural evaluation algorithm and an iterative gradient optimization algorithm used in image restoration and statistical filtering are presented. The relationship between the two algorithms is studied. We show that under the symmetric positive-definite condition, a condition easily satisfied in restoration and filtering, intra-pixel sequential processing (IPSP) of model-based neuron evaluation is equivalent to the iterative gradient optimization algorithm. We also show that although both methods provide feasible solutions to fast spatial domain implementation of restoration and filtering techniques, the iterative gradient algorithm is in fact more efficient than the IPSP neuron evaluation method. Visual examples are provided to compare the performance of the two approaches.
Ling Guan, "Model-based neural evaluation and iterative gradient optimization in image restoration and statistical filtering," Journal of Electronic Imaging 4(4), (1 October 1995). http://dx.doi.org/10.1117/12.217268
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KEYWORDS
Neurons

Image filtering

Optimization (mathematics)

Model-based design

Image restoration

Statistical modeling

Digital filtering

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