Image super-resolution based on multi-grained cascade forests

Abstract

Although neural networks are most commonly used in the field of image super-resolution (SR), methods based on decision trees are still discussed. These kinds of algorithm need less time to compute than others because of their simple structure but still yield high quality image SR. In this paper, we propose an SR algorithm using the multi-grained cascade forest (SRGCF) method. Our algorithm first uses multi-grained scanning to process the spatial relationships of image features, thus the representational learning ability is improved. During the reconstruction process, the image obtained by cascade forest training is used as the input of the next training, therefore, the image features are continuously emphasized. The training of the cascade forest ends when the evaluation value is optimal. Because the decision tree uses a divide-and-conquer strategy, the SR of an image is improved in an iterative manner simply and quickly. Compared with existing methods, our method not only avoids the tradeoff between reconstruction quality and run time, but also has a good generalization capability. It can be quickly applied to the many cases of image SR.