A novel hierarchical random forests based super-resolution (SRHRF) method is proposed to learn statistical priors from external training images. Each layer of ...

The low-resolution image can be reconstructed into a high-resolution image through the super-resolution reconstruction technique.

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using ... ➢ Exploit non-local self-similar patterns using random forests. ➢ SRHRF generates a ...

Bibliographic details on SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests.

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests. Conference Paper. Full-text available. Jul 2017.

Article "SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests" Detailed information of the J-GLOBAL is an ...

SRHRF+: Self-example enhanced single image super-resolution using hierarchical random forests. JJ Huang, T Liu, P Luigi Dragotti, T Stathaki. Proceedings of ...

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests. Jun-Jie Huang,. Tianrui Liu,. Pier Luigi Dragotti,. Tania ...

SRHRF+: Self-Example Enhanced Single Image Super-Resolution Using Hierarchical Random Forests, Huang, Jun-Jie; Liu, Tianrui; Dragotti, Pier Luigi; Stathaki ...

Співавтори ; SRHRF+: Self-example enhanced single image super-resolution using hierarchical random forests. JJ Huang, T Liu, P Luigi Dragotti, T Stathaki.