Fast image super-resolution via Randomized Multi-split Forests

Zhi Song Liu, Wan Chi Siu, Yui Lam Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
ISBN (Electronic)9781467368520
Publication statusPublished - 25 Sept 2017
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 28 May 201731 May 2017


Conference50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/TerritoryUnited States


  • Image super-resolution
  • learning
  • multi-split
  • random forests
  • randomization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Fast image super-resolution via Randomized Multi-split Forests'. Together they form a unique fingerprint.

Cite this