Selection of regularization parameter based on generalized cross-validation in total variation remote sensing image restoration

2010 ◽  
Author(s):  
Peng Liu ◽  
Dingsheng Liu
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Total Variation ◽  
Cross Validation ◽  
Regularization Parameter ◽  
Remote Sensing Image ◽  
Generalized Cross Validation ◽  
Selection Of

Stacked lossless deconvolutional network for remote sensing image restoration

2020 ◽  
Vol 14 (01) ◽  
pp. 1
Author(s):  
Changyeop Shin ◽  
Minbeom Kim ◽  
Sungho Kim ◽  
Youngjung Kim
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Remote Sensing Image

Smoothing Noisy Data Using Dynamic Programming and Generalized Cross-Validation

1988 ◽  
Vol 110 (1) ◽  
pp. 37-41 ◽  
Author(s):  
C. R. Dohrmann ◽  
H. R. Busby ◽  
D. M. Trujillo
Keyword(s):  
Dynamic Programming ◽  
Cross Validation ◽  
Present Model ◽  
Noisy Data ◽  
Smoothing Parameter ◽  
Spline Functions ◽  
General Context ◽  
Simple Extension ◽  
Generalized Cross Validation ◽  
Selection Of

Smoothing and differentiation of noisy data using spline functions requires the selection of an unknown smoothing parameter. The method of generalized cross-validation provides an excellent estimate of the smoothing parameter from the data itself even when the amount of noise associated with the data is unknown. In the present model only a single smoothing parameter must be obtained, but in a more general context the number may be larger. In an earlier work, smoothing of the data was accomplished by solving a minimization problem using the technique of dynamic programming. This paper shows how the computations required by generalized cross-validation can be performed as a simple extension of the dynamic programming formulas. The results of numerical experiments are also included.

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