lecture18_2 - CS 519 Signal Image Processing Reconstruction...

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1 CS 519 – Signal & Image Processing Reconstruction Image Reconstruction Reconstruction is the process of attempting to recreate the original signal given a corrupted one Terms: Scene: the “real world” Image: a (possibly corrupted) picture of a scene Image reconstruction attempts to recreate the scene from an image
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2 Required Knowledge Reconstruction algorithms usually use one or more of Knowledge about the process that corrupted the image Knowledge about properties of the original scene Examples: Deconvolution requires knowledge of the point spread function Weiner filtering requires an estimate of the strength of the noise Knowledge About Corruption Process Knowledge about the corruption process puts limits on reconstruction Usually though of a “fitting the data”: the reconstructed image can’t vary too much from the original corrupted image Example: Assuming white noise with standard deviation σ , the probability of getting noisy image g from scene f is σ = i g f i i e f g p 2 2 / ) ( ) | (
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