1. In a method for determining wavelet scale weighting coefficients, the improvement comprising: performing supervised training, including wavelet transformation, iterative inverse wavelet transformation and error analysis, on at least one training image derived from sampled radiation to determine three or more non-zero wavelet scale weighting coefficients, comprising, generating a teacher image based on a respective training image derived from sampled radiation, including identifying in said teacher image true locations of indicia in said respective training image, and defining a training-free zone around each of the identified true locations of said indicia in said teacher image; and selecting said three or more non-zero wavelet scale weighting coefficients based on iterative inverse wavelet transformation and error analysis performed in relation to each said training-free zone defined in said teacher image; wherein said step of selecting said three or more non-zero wavelet scale weighting coefficients includes the substeps of a) selecting first candidate weighting coefficients, b) generating a reconstructed training image based on the first candidate wavelet scale weighting coefficients and said respective training image, c) determining a first error using said reconstructed image and said respective teacher image, d) selecting second candidate weighting coefficients based on the first error and repeating steps b) and c) for the second candidate weighting coefficients until the error determined in step c) is within a specified error condition; and wherein said substep of determining a first error includes the steps of: determining a first error component based on a respective maximum value in each training-free zone in said at least one reconstructed image, determining a second error component based on differences between intensity values of said respective teaching image and said reconstructed training image outside the training-free zones, determining a ratio between a total number of said indicia identified and a number of pixels in the reconstructed training image outside the training-free zones, providing a balanced error by multiplying said ratio to the second error component, and calculating a total error in said at least one reconstructed image by combining the balanced error and the first error component.