Here are three problems that I can't understand:

  1. Usually what does de-correlation mean in image-processing?

  2. What's the benefit of de-correlation when it comes to image splicing?

  3. Is there some relationship among neighbouring coefficients? And I read a paper which says coefficients difference matrix(got by coefficients subtracting its neighbor) can eliminate the relationship of the coefficients,How to understand this?

I would really appreciate if someone can help me!


1 Answer 1

  1. The term "decorrelation" generally accounts for processing that reduce autocorrelation within single, or cross-correlation within a group of signals or images. Doing that, it should preserve important features in the data.

  2. In other terms, images often contains simpler "objects" (in terms of morphology: bumps, edges, textures) that are mixed, or distorted. Generally, unmixing them via decorrelation somehow simplify further processing.

  3. Generally yes, unless you have uncorrelated noise in your images. A classical and simple model is an order-one Markov or autoregressive process. Basically a pixel somehow depends (with a parameter $\rho$) on the past pixel, with uncertainty. If $\rho$ is close to $0$, you are already decorrelated. If $\rho$ is close to $1$ (typically $0.90-0.95$), the data is quite correlated.

The DCT was meant for diagonalizing the resulting autocovariance matrices with Toeplitz structure, to give fast estimates of their eigenvectors. Transforming a fat autocovariance matrix into a thin, close to a diagonal, matrix is an instance of decorrelation.


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