Classification-based hybrid filters for image processing

The paper proposes a new type of nonlinear filters, classification-based hybrid filters, which jointly utilize spatial, rank order and structural information in image processing. The proposed hybrid filters use a vector containing the observation samples in both spatial and rank order. The filter coefficients depend on the local structure of the image content, which can be classified based on the luminance pattern in the filter window. The optimal coefficients for each class are obtained by the Least Mean Square optimization. We show that the proposed classification-based hybrid filters exhibit improved performance over linear filters and order statistic filters in several applications, image de-blocking, impulsive noise reduction and image interpolation. Both quantitative and qualitative comparison have also been presented in the paper.