Learning Hierarchical Decision Trees for Single Single-Image Super-Resolution Resolution
Abstract: Sparse representation has been extensively studied for image super-resolution super (SR), and it achieved great improvement. Deep Deep-learning-based based SR methods have also emerged in the literature to pursue better SR results. In this paper, we propose to use a set of decision tree strategies for fast and high high-quality quality image SR. Our proposed SR using decision tree (SRDT) method takes the divide-and-conquer divide strategy, which performs a few simple binary tests to classify an input lowlow resolution (LR) patch into one of the leaf nodes and directly multiplies this LR patch with the regression model at that leaf node for regression. Both the classification process and the regression process take an extremely small amount of computation. To further boost the SR results, we introduce a SR using hierarchical decision trees (SRHDT) method, which cascades multiple layers of decision trees for SR and progressively refines the estimated high-resolution high image. Inspired by the random forests approach, which combines regression models from an ensemble of decision trees, we propose to fuse regression models from relevant leaf nodes within the same decision tree to form a more robust approach. The SRHDT metho method d with fused regression model (SRHDT_f) improves further the SRHDT method by 0.1 0.1-dB dB in PNSR. Our experimental results show that our initial approach, the SRDT method, achieves SR results comparable to those of the sparse-representation representation-based method and the deep-learning learning-based method, but our method is much faster. Furthermore, our enhanced version, the