Image super-resolution via feature-augmented random forest
作者：
Highlights：
• Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the features working in RF is overlooked in the literature.
• In this paper, we present a novel feature-augmented random forest (FARF) method for image super-resolution, where the non-linear gradient magnitudes are proposed to augment the features used in RF, generalized locality-sensitive hashing (LSH) is used to replace principal component analysis (PCA) for feature dimensionality, and different feature recipes are formulated on different processing stages in an RF.
摘要
•Recent random-forest (RF)-based image super-resolution approaches inherit some properties from dictionary-learning-based algorithms, but the effectiveness of the features working in RF is overlooked in the literature.•In this paper, we present a novel feature-augmented random forest (FARF) method for image super-resolution, where the non-linear gradient magnitudes are proposed to augment the features used in RF, generalized locality-sensitive hashing (LSH) is used to replace principal component analysis (PCA) for feature dimensionality, and different feature recipes are formulated on different processing stages in an RF.
论文关键词：Random forest,Gradient magnitude filter,Clustering and regression,Image super-resolution,Weighted ridge regression
论文评审过程：Received 23 February 2018, Revised 1 December 2018, Accepted 3 December 2018, Available online 12 December 2018, Version of Record 17 December 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.12.001
