Fast image interpolation with decision tree

This paper proposes a fast image interpolation method using decision tree. This new fast image interpolation with decision tree (FIDT) method can achieve state-of-the-art image interpolation performance and requires only 10% computational time of the soft adaptive interpolation (SAI) method. During training, the proposed method recursively divides the training data at a non-leaf node into two child nodes according to the binary test which can maximize the information gain of a division. At the end, for each of the leaf node, a linear regression model is learned according to the training data at that leaf node. In the image interpolation phase, input image patches are passed into the learned decision tree. According to the stored binary test at each non-leaf node, each input image patch will be classified into its left or right child node until a leaf node is reached. The high-resolution image patch of the input image patch can then be predicted efficiently using the learned linear regression model at the leaf node.