Practical application of random forests for super-resolution imaging

In this paper, a novel learning-based single image super-resolution method using random forest is proposed. Different from example-based super-resolution methods which search for similar image patches from an external database or the input image, and the sparse representation model based methods which rely on the sparse representation, this proposed super-resolution with random forest (SRRF) method takes the divide-and-conquer strategy. Random forest is applied to classify the training LR-HR patch pairs into a number of classes. Within every class, a simple linear regression model is used to model the relationship between the LR image patches and their corresponding HR image patches. Experimental results show that the proposed SRRF method can generate the state-of-the-art super-resolved images with near real-time performance.