@@ -713,19 +713,27 @@ public class CorrectionFPN {
order,// final int order,
debugLevel);// final int debugLevel){
finaldoubleum_sigma=5;
finalintgood_margins=12;
finaldoublethreshold=20;// 5;
finaldoublew_blur=2.5;// 5;
finaldoubleum_sigma=clt_parameters.imp.cuas_w_um_sigma;// 1.5; // run UM to approximate LoG
finalintgood_margins=clt_parameters.imp.cuas_w_good_margins;// 12; // consider defined values near image margins always good (do not have anything better anyway). Does not apply to NaN
// // areas that will be filled from different poses
finaldoublethreshold=clt_parameters.imp.cuas_w_threshold;// 20; // threshold to cut off high variations (it will be squared to compare to squared difference values)
finaldoublescale_thresh=clt_parameters.imp.cuas_w_scale_thresh;// 1.5; // Allow over-threshold values before GB, to block them completely after GB thresholding
finalbooleanexpand_max=clt_parameters.imp.cuas_w_expand_max;// true; // replace diff values by max of neighbors (fill zero-square gaps)
finaldoubleoutliers_w=clt_parameters.imp.cuas_w_outliers;// 0.001; // Assign weights to outliers to avoid NaNs in bad areas
finaldoublew_blur=clt_parameters.imp.cuas_w_blur;// 1.0; // blur thresholded squared values to smooth transitions.
publicdoublecuas_invert_tolerance=0.001;// Finish enhancing when last change was lower than
publicintcuas_invert_gap2=10;// Maximal dual gap size for inversion (depends on scanning radius in tiles) <0 = use maximal possible
publicintcuas_invert_gap2=10;// Maximal dual gap size for inversion (depends on scanning radius in tiles) <0 = use maximal possible
// Calculation of FPN weights for averaging different poses, detecting high gradients of the real image on back-propagated average where image difference
// may have large errors causes my discrete pixels
publicdoublecuas_w_um_sigma=1.5;// run UM to approximate LoG
publicintcuas_w_good_margins=12;// consider defined values near image margins always good (do not have anything better anyway). Does not apply to NaN
// areas that will be filled from different poses
publicdoublecuas_w_threshold=20;// threshold to cut off high variations (it will be squared to compare to squared difference values)
publicdoublecuas_w_scale_thresh=1.1;// Allow over-threshold values before GB, to block them completely after GB thresholding
publicbooleancuas_w_expand_max=true;// replace diff values by max of neighbors (fill zero-square gaps)
publicdoublecuas_w_outliers=0.0001;// Assign weights to outliers to avoid NaNs in bad areas
publicdoublecuas_w_blur=1.0;// blur thresholded squared values to smooth transitions.
publicbooleancuas_debug=false;// save debug images (and show them if not in batch mode)
publicbooleancuas_step_debug=false;// save debug images during per-step cuas recalculation (and show them if not in batch mode)
...
...
@@ -2040,6 +2052,22 @@ min_str_neib_fpn 0.35
gd.addNumericField("Inversion gap (dual size)",this.cuas_invert_gap2,0,3,"tiles",
"Maximal dual gap size for inversion (depends on scanning radius in tiles) .");
gd.addMessage("=== Masking out high LoG areas for averaging for different poses ===");
"Consider defined values near image margins always good (do not have anything better anyway). Does not apply to NaN areas that will be filled from different poses.");
