Reducing reconstruction error of classified textural patches by integration of random forests and coupled dictionary nonlinear regressors: with applications to super-resolution of abdominal CT images.
Mahdieh AkbariAmir Hossein ForuzanYen-Wei ChenHongjie HuPublished in: Int. J. Comput. Assist. Radiol. Surg. (2021)
Keyphrases
- random forests
- super resolution
- image patches
- sparse representation
- low resolution
- high resolution
- random forest
- image reconstruction
- sparse coding
- decision trees
- reconstructed image
- motion estimation
- machine learning algorithms
- logistic regression
- ensemble methods
- high quality
- natural images
- image processing
- prediction accuracy
- image classification
- machine learning methods
- higher order
- pattern recognition
- face recognition
- data sets
- dimensionality reduction
- optical flow
- image segmentation
- computer vision