Robust Fuzzy Content Based Regularization Technique in Super Resolution Imaging

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a new robust fuzzy super resolution approach. Our approach, firstly registers two input images using SIFT-BP-RANSAC registration. Secondly, due to the importance of information gain ratio in the SR outcomes, the fuzzy regularization scheme uses the prior knowledge about the low-resolution image to add the amount of lost details of the input images to the registered one using the common linear observation model. Due to this fact, our approach iteratively tries to make a prediction of the high-resolution image based on the predefined regularization rules. Afterwards the low-resolution image is made out of the new high-resolution image. Minimizing the difference between the resulted low-resolution image and the input low-resolution image will justify our regularization rules. Flexible characteristics of fuzzy regularization adaptively behave on edges, detailed segments, and flat regions of local segments within the image. General information gain ratio also should grow during the regularization. Our fuzzy regularization indicates independence from the acquisition model. Consequently, robustness of our method on different ill-posed capturing conditions and against registration error noise compensates the shortcomings of same regularization approaches in the literature. Our final results show reduced aliasing achievements in comparison with similar recent state of the art works.