TAILIEUCHUNG - CS 450: Image reconstruction

CS 450: Image reconstruction includes about required knowledge; knowledge about the corruption process; sattistical reconstruction; bayestical recontrucstion; balancing the data and prior term; other recontrucstion methods. | CS 450 Image Reconstruction 1 Image Reconstruction Reconstruction is the process of attempting to recreate the original signal given a corrupted one. Terms in Image Reconstruction Scene the real world Image a possibly corrupted picture of a scene Image reconstruction attempts to recreate the scene from an image. CS 450 Image Reconstruction 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 required knowledge of the point spread function. Weiner filtering requires an estimate of the strength of the noise. CS 450 Image Reconstruction 3 Knowledge About the Corruption Process I Knowledge about the corruption process puts limits on reconstruction. Usually thought of as fitting the data the reconstructed image can t vary too much from the original corrupted image. Example Assuming white noise with standard deviation Ơ the probability of getting noisy image g from scene f is P g jf n 9t 2 2

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• CS 450 Image reconstruction
• Knowledge about the corruption process
• Sattistical reconstruction
• Bayestical recontrucstion
• Balancing the data and prior term
• Other recontrucstion methods