View source: R/threeStageParSel.r

threeStageParSel | R Documentation |

Select the bandwidth value for the image restoration method implemented in the function threeStage

`threeStageParSel(image, bandwidth, edge1, edge2, nboot, blur=FALSE)`

`image` |
A square matrix object of size n by n, no missing value allowed. |

`bandwidth` |
Bandwidth values to be chosen from. Each of these values need to be an positive integer which specifies the number of pixels used in the local smoothing. |

`edge1` |
A matrix of 0 and 1 of the same size as image represents detected step edge pixels. |

`edge2` |
A matrix of 0 and 1 of the same size as image represents detected roof/valley edge pixels. |

`nboot` |
Required when blur is TRUE. Unused when blur is FALSE. An positive integer to specify the number of bootstraps to perform. See Qiu and Kang (2015) Statistica Sinica for details. |

`blur` |
TRUE if the image contains blur, FALSE otherwise. If TRUE, the hybrid selection method proposed in Qiu and Kang (2015) Statistica Sinica is used. If FALSE, the leave-one-out cross validation is used. |

Returns a list of the selected bandwdith, and a matrix of CV values with each entry corresponding to each choice of bandwdith.

Qiu, P., and Kang, Y. "Blind Image Deblurring Using Jump Regression
Analysis," *Statistica Sinica*, **25**, 2015, 879-899.

```
data(peppers) # Peppers image is bundled with the package and it is a
# standard test image in image processing literature.
# Not Run
#step.edges <- stepEdgeLLK(peppers, 9, 17) # Step edge detection
#roof.edges <- roofEdge(peppers, 6, 3000, edge1=step.edges) # Roof edge detection
#set.seed(24)
#parSel <- threeStageParSel(image = peppers, edge1 = step.edges, edge2 = roof.edges,
#bandwidth = 4, nboot = 1, blur = TRUE) # Time consuming
```

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