Please use this identifier to cite or link to this item:
|Fast image super-resolution via Randomized Multi-split Forests
|Siu, Wan Chi
|Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS)
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.
|CIHE Affiliated Publication:
|Appears in Collections:
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.