Fast image super-resolution via Randomized Multi-split Forests
Zhi-Song Liu, Wan-Chi Siu, Yui‐Lam Chan · 2017
This paper proposes a novel learning-based image Super-Resolution via a Randomized Multi-split Forests model (SRRMF). The proposed method uses the LR-HR training patch pairs to model the nonlinear patch manifold into a pairs of linear subspaces. The key idea of this approach is to use several decision trees split randomly the training data into different classes. A linear regression model is learnt to map the relationship between LR and HR patches at the end of the leaf nodes. In order to make full use of the generalization ability of the random forests, we randomize the grow of the decision tree to cover more possibilities. Furthermore, we modify the splitting function by using Multi-Split Binary Test (MSBT) function so that we can use more feature information to derive more accurate classification result to match patch subspace. Extended experimental results show that image super-resolution using our proposed method can achieve the state-of-the-art super-resolution performance with reduced computation time.