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Topic: Singular value decomposition in noise reduction
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Posts: 1
Registered: 10/17/09
Singular value decomposition in noise reduction
Posted: Oct 18, 2009 7:11 AM
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SVD, given matrix A. A = UE(V^T)

I understood that the singular values (diagonal E) are the noise and need to be reduced to obtain a clearer image. But how does reducing the singular values actually affect the overall picture? Is it because of the change in A, U and V?

From my understanding, the bigger the singular values are, the more noise there are in the image. Hence, after obtaining diagonal E from A, we reduce the E and then compute A, is it? Which of these (A, U, V) actually makes the new image clearer?

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