APW Dithering

Developed by the Team - Developed Algorithms

The APW dithering method is a simple variation of Veryovka-Buchanan dithering method. Here we describe into two parts this 'new method'.

Segmentation using Iterative Selection

The idea is to provide an estimate of the average gray level of both the background  (T_b) and the objects (T_o), and to use the average of these two levels as threshold: T = (T_o+T_b)/2. Initially these values are guesses based on know properties of the image. If is know that objects are dark and the background is lighter, the initial values could be T_b= 0 e T_o=255. Sometimes values from the four corners are assumed to be background pixels. It is even possible to use the overall mean gray levels as the initial threshold T and the produce a guess for T_b and T_o on the next iteration.

The next step is to refine the values de T_b and T_o using o threshold T. Assuming that dark regions are objects, T_o is recalculated to be mean value of all pixels less than T. Similarity, the new value of T_b is the mean value of all pixels with a value greater than T.  This produce a better estimate of the mean levels, and these in turn should produce a better estimate threshold which is now recomputed, as before, as T = (T_o+T_b)/2, using the new values for T_o and T_b.

This entire process is repeated until the same threshold value T is produced on two consecutive iterations, at which point T is presumed to be a good threshold for the image.

Threshold in dithering

After of find the threshold to segment the image, using the follow algorithm:

            If Pixel of Image < Threshold then

                Use texture one

Else

    Use texture two 

The texture one or two is used to be applied to the image.  After the segmentation the method of the Veryovka-Buchanan is usually applied.

Exemplifying: