Image Texture Synthesis-by-Analysis using Moving-Average Models

Texture synthesis is a necessary component of realistic scene generation. In particular, it is necessary for the simulation of image backgrounds for the testing of automatic target recognizers. We present a synthesis-by-analysis model for texture replication or simulation. This model can closely replicate a given textured image or produce another image which, although distinctly di erent from the original, has the same general visual characteristics and the same rst and second-order gray-level statistics as the original image. In e ect, such a synthetic image looks like a continuation of the original scene; as if another picture of the scene were taken adjacent to the original. The texture synthesis algorithm proposed herein contains three distinct components: A moving-average (MA) lter, a lter excitation function, and a gray-level histogram. The analysis portion of texture synthesis algorithm derives the three from a given image. The synthesis portion convolves the MA lter kernel with the excitation function, adds noise, and modi es the histogram of the result. The advantages of this texture model over others include conceptually and computationally simple and robust parameter estimation, inherent stability, parsimony in the number of parameters, and synthesis through convolution. We herein (1) describe a procedure for deriving the correct MA kernel using a signal enhancement algorithm; (2) demonstrate the e ectiveness of the model by using it to mimic several diverse textured images; (3) discuss its applicability to the problem of infrared background simulation; and (4) include detailed algorithms for the model's implementation.