Image Super-resolution via Weighted Random Forest

This paper proposes a novel learning-based image super-resolution via a weighted random forest model (SWRF). The proposed method uses the LR-HR training data to train a random forest model. The underlying idea of this approach is to use several decision trees to classify the training data based on a simple splitting threshold value at each class. A linear regression model is learnt to map the relationship between LR and HR patches. During the up-sampling process, to obtain a more robust super-resolved HR image, instead of averaging the linear regression models from different trees, a biased weighting vector is learnt to adaptively super-resolve the LR image. Furthermore, we improve this proposed image super-resolution method via a weighted random forest model with rotation (SWRF-f) to further improve the super-resolution quality. Sufficient experimental results prove that the proposed approach can achieve the state-of-the-art super-resolution performance with reduced computation time.