#5967. 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

August 2026publication date
Proposal available till 07-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Surgery;
Computer Graphics and Computer-Aided Design;
Radiology, Nuclear Medicine and Imaging;
Health Informatics;
Computer Science Applications;
Computer Vision and Pattern Recognition;
Biomedical Engineering;
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Abstract:
Purpose: Random forests and dictionary-based statistical regressions have common characteristics, including non-linear mapping and supervised learning. To reduce the reconstruction error of high-resolution images, we integrate random forests and coupled dictionary learning. Methods: Textural differences of image blocks are considered by the classification of patches using an Auto-Encoder network. The proposed algorithm partitions an input LR image by 5 ? 5 blocks and classifies training patches into six categories. A single random forest regressor is then trained corresponding to each class. The output of an RF is considered as an initial estimate of the HR slice. If a slice’s representation is sparse in the Discrete Cosine Transform domain, the initial reconstructed image is further improved by a coupled dictionary.
Keywords:
Abdominal CT images; Auto-encoders; Coupled dictionary learning; Random forests; Super-resolution

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