A Method Based on Piecewise Linear Models for Accurate Restoration of Images Corrupted by Gaussian Noise

Piecewise linear (PWL) models are very attractive for image processing due to their simplicity and effectiveness. A new filtering architecture adopting multiparameter PWL functions is proposed for accurate restoration of images corrupted by Gaussian noise. The filtering performance is analyzed by taking into account the different behavior from the point of view of noise removal and detail preservation. The sensitivity to a change of the parameter settings is also investigated. In the new approach, the parameter values are automatically selected by resorting to a procedure that estimates the standard deviation of the Gaussian noise. Results dealing with different test images and noise variances show that the method yields a very accurate restoration of the image data