Deconvolution is a technique in signal or image processing that is applied when data is difficult to read due to spreading and blurring of corrupt images and experimental results. Through deconvolution, the investigator can gain access to the true and uncorrupted phenomenon. Advantages include reduced noise sensitivity and super resolving capabilities that have lead to important advances such as the explosive development of computer-based communications, neural networks, the discovery of the nucleus of Halley's comet and ...
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Deconvolution is a technique in signal or image processing that is applied when data is difficult to read due to spreading and blurring of corrupt images and experimental results. Through deconvolution, the investigator can gain access to the true and uncorrupted phenomenon. Advantages include reduced noise sensitivity and super resolving capabilities that have lead to important advances such as the explosive development of computer-based communications, neural networks, the discovery of the nucleus of Halley's comet and new insights into cell biology. This second edition addresses both the newest computer hardware applications and the implementation of modern non-linear constrained methods. The text conveys an understanding of the field while providing a selection of effective, practical techniques. The authors assume only a working knowledge of calculus, and emphasizing practical applications over topics of theoretical interest, focusing on areas that have been pivotal to the evolution of the most effective methods.
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