Parametric Wiener Filter Based on Image Power Spectrum Sparsity

Abstract

A simple and effective denoising method for a spectral subtractive (SS)-type parametric Wiener filter (PWF) for a blind condition is proposed. A simple noise estimation method is used to estimate the noise variance directly from a noisy image. Preliminary experiments with trained images are conducted to find the best parameters for the PWF. The PWF gives the highest performance with the best parameter setting. However, in practice, it is difficult to know the best parameters because they depend on the characteristics of the image. To estimate the best parameters for the PWF, therefore, a novel tool named image power spectrum sparsity, which is not influenced by the noise level, is derived. The parameters for the PWF are set according to the power spectrum sparsity. To demonstrate the effectiveness of the PWF, untrained images are used. The experimental results show that the proposed method gives a good performance with the shortest computational time among the WF methods to restore an image under a blind condition.