Cascaded random forests for fast image super-resolution

Abstract

Due to the development of deep learning, image super- resolution has achieved huge improvement on both subjective and objective qualities. However, the computation is still a problem for real-time applications. In this paper, we propose a Cascaded Random Forest for Image Super-Resolution (CRFSR) which screens sufficient simple features to train a much robust and efficient model for image super-resolution. To further boost up the super-resolution performance, an extra Gaussian Mixture Model (GMM) based layer is added as the final refinement. Extensive experimental results show that the cascaded decision trees continue performing better when more features are selected for refinement. The analysis on both computation time and reconstruction fidelity indicates the superior performance of our proposed CRFSR and CRFSR+ with extra GMM-based layer on natural images.