A New Single Image Super-resolution Method Using SIMK-based Classification and ISRM Technique

Peiqi Duan, Anlong Ming, Xuejing Kang, Chao Yao

Abstract: Single image super-resolution (SR) technique is widely used to estimate high-resolution (HR) images from low-resolution (LR) ones. As a research hotspot, many example-based SR methods achieve superior results by learning class-mapping-kernels from classified external LR-HR patch-pair samples. However, in these methods, the classification of samples is generally based on the features of LR patch, and the interference of ill-samples to learn class-mapping-kernels is ignored as well. In this paper, we propose a new SR method with Sample Individual Mapping-Kernel (SIMK) based classification and Ill-Sample Removal Mechanism (ISRM) in learning LR-HR mapping. In the proposed sample classification, we use the SIMK feature which is the LR-to-HR mapping kernel of each sample, to classify samples and obtain more reasonable sample sets for mapping-learning. To prevent overfitting and reduce the complexity of SIMK-based-classification, samples are pre-categorized by relative pixel values of LR patch. In the mapping-learning process, the ill-samples which are far away from the classification center are removed to improve the validity of class-mapping-kernels. In addition, for each testing LR patch, the optimal class is assigned reasonably based on a probabilistic decision model learned from Naive Bayes Classifier. Comparing with state-of-the-art methods, our SR method achieves both visual and performance improvement.