SVD-Based Image De-Nosing with the Minimum Engergy Model

This paper proposes a new solution integrating energy function into singular value decomposition (SVD) for image de-noising. The singular values on the diagonal matrix obtained through SVD represent different components in image. By selecting the proper singular values that represent signal and discarding the ones that represent noise, the additive noise of an image can be eliminated effectively. In order to obtain the optimal number of the singular values for image reconstruction and to eliminate the noise, the paper presents a minimum energy model. This model is used to obtain the optimum number for de-noising through calculating the minimum in the defined energy curve. The experiment results show that the established model is effective in the circumstance that the image has simple/regular structure/pattern.