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.
With the advent of high-definition television, video phone, Internet and video on PCs, media content has to be displayed with different resolutions and high quality image interpolation techniques are increasingly demanded. Traditional image interpolation methods usually use a uniform interpolation filter on the entire image without any discrimination and they tend to produce some undesirable blurring effects in the interpolated image. Some content adaptive interpolation methods have been introduced to achieve a better performance on specific image structures. However, these content adaptive methods are limited to fitting image data into a linear model in each image structure. We propose extending the linear model to a flexible non-linear model, such as a multilayer feed-forward neural network. This results in a new interpolation algorithm using neural networks with coefficients based on the known pixel classification. Due to the fact that the number of classes using the pixel classification increases exponentially along with the filtering aperture size, we further introduce an efficient method to reduce the number of the classes. The results show that the proposed algorithm demonstrates more robust estimation in image interpolation and gives an additional improvement in the interpolated image quality. Furthermore, the work also shows that the use of pre-classification limits the complexity of the neural network while still achieving good results.