1 CV_SMOOTH CV_SMOOTH This task smoothes an image using a cross-validation algorithm. The idea behind the algorithm is to use all data points except one to estimate the value of this one point. The difference between estimated and measured values is used to determine the noise level in the map, and therefore the appropriate amount of smoothing. The smoothing is non uniform, and depends on the signal to noise: low level emission is smoothed more than strong peaks. The present algorithm has a number of restrictions which we hope to suppress in future versions: - It flatly refuses to do anything if the noise is correlated between adjacent points, but it takes quite a long time to find this... Of course this always occurs for interferometric maps. Hence, it is possible to add a small amount of extra noise to the image to by-pass this stupid restriction. - It is a basically 1D algorithm. The algorithm smoothes along axis 1 then 2 first, and in a second pass along 2 then 1. It takes the average of the two results to produce the smoothed image, and keeps the difference which should be representative of the errors. A 2D generalisation of the algorithm exists (Girard D., 1987, Rapport de Recherche RR 669-M, TIM3. Universite de Grenoble), and we hope to implement it in the near future. 2 Z_NAME$ CV_SMOOTH: Z_NAME$ This is the name of the input map. Cubes are probably not supported at present. 2 Y_NAME$ CV_SMOOTH: Y_NAME$ This is the name for the output difference map. It is not deleted automatically. 2 X_NAME$ CV_SMOOTH: X_NAME$ This is the name of the output smoothed map. 2 NOISE$ CV_SMOOTH: NOISE$ This is the rms (in map units) of the optional additional gaussian noise. The additional noise is used to fool the algorithm in cases where the noise is correlated between adjacent pixels. Specify 0 if you don't want to add extra noise to your input map.