Quantum-computation-inspired reverse analysis texture synthesis

We present a simple model for characterizing and synthesizing binary texture, whose main appeal is that it introduces the concept of reverse analysis, directly inspired by Quantum Computation. This approach allows to overcome the need of a synthesis algorithm, in the classical sense of the term. Instead, we compute, for every possible N×N pattern (N small), a set of M texture parameters. By selecting some texture parameters´ values in the transformed space, we can readily identify the set of local patterns compatible with those parameters (reverse analysis). Then, we sample the local texture pattern by randomly choosing among them, same as if we were observing entangled qubit patterns. To provide visual continuity of adjacent patterns, we force them to match their pixels in overlapping areas. Although the texture model presented here is extremely simple (M = 3), results are promising, and the same concept could be applied to more sophisticated models.