Efficient fuzzy composite predictive scheme for effectual 2-D up-sampling of images for multimedia applications

In this paper, a highly nonlinear, fuzzy logic based, composite scheme is proposed by combining a pre-processing and a post-processing operation to efficiently restore high frequency (HF) and very high frequency (VHF) details in an up-scaled image. The blurring in case of an up-sampled image is caused by the degradation of HF and VHF image details that correspond to fine details and edge regions during the up-sampling process. The degradation of HF and VHF image details is more significant than that of the flat and slowly varying regions. In order to resolve this problem effectively, a fuzzy composite scheme is developed which is based on the inverse modeling approach of HF degradation. During the pre-processing operation, the VHF components of an image are boosted up using recursive Laplacian of Laplacian (LOL) operator prior to image up-scaling. Subsequent to the image up-scaling, a fuzzy local adaptive Laplacian post-processing scheme is used which enhances the HF image details more than the low frequency image details based on local statistics in the up-scaled image. The HF restoration performance of the fuzzy based composite scheme is enhanced by improving its nonlinearity through the variations of different parameters of the fuzzy inference system (FIS) such as slope, width and the number of input-, and output membership functions. The effective fusion of pre-processing and post-processing operations makes the proposed scheme much effective to tackle the non-uniform blurring than the standalone pre-processing and post-processing techniques. Experimental results reveal that the proposed composite scheme gives much less blurring in comparison to the standalone schemes and performs better than most of the widely used interpolation schemes in terms of objective and subjective measures.