Single Frame Super Resolution: Fuzzy Rule-Based and Gaussian Mixture Regression Approaches

High quality image zooming is an important problem. There are many methods that use multiple low resolution (LR) frames of the same scene with different sub-pixel shifts as input to generate the high resolution (HR) images. Now a days single frame super resolution (SR) methods that use just one LR image to obtain the HR image has become popular. In this talk we shall discuss a novel fuzzy rule based single frame super resolution method. This is a patch based method, where each LR patch is replaced by a HR patch generated by a Takagi-Sugeno type fuzzy rule-based system. We shall discuss in details the generations of the training data, the initial generation of the fuzzy rules, their refinement and how to use the rules for generation of SR images. In this context we shall also develop a Gaussian Mixture Regression (GMR) model for the same problem. Both the fuzzy rule based system and GMR are found to be quite effective. Comparison of performance of the fuzzy rule-based system with five existing methods as well as with the GMR method in terms of the several quality criteria demonstrates the superior performance of the fuzzy rule-based system.