Modern image and signal processing methods strive to maximize signal to nose ratios, even in the presence of severe noise. Frequently, real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform and fractal theory will offer a viable alternative as some early work in the area has indicated. An architecture of a software system is suggested to implement an improved scheme for the analysis, representation, and processing of images. The scheme is based on considering the segments of images as wavelets and fractals so that small details in the images can be exploited and the data can be compressed. The objective is to improve this scheme automatically and rapidly decompose a 2D image into a combination of elemental images so that an array of processing methods can be applied. Thus, the scheme offers potential utility for analysis of image could be the patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets.