A soft measure for identifying structure from randomness in images

This paper presents a novel measure for identifying strong structure features, such as edges, from randomness, such as regions predominated by noise, within an image. The proposed structural measure is localized in space and scale; for a given scale, it gives values close to one in the vicinity of strong structures and close to zero in regions predominated by noise. The proposed structural measure is a primitive operation that can be used in a wide variety of image analysis techniques to identify regions which has structure; for example, motion estimation is more meaningful in structured regions than in regions filled with noise. The first innovation in this work is in converting an image into a ternary feature map that are rather resistant to noise and changes in illumination. The second is the structural measure, which is derived from the degree of non-uniformity amongst the magnitudes of the DFT coefficients obtained over a small window within the ternary maps. In this work, we show that the proposed structural measure is robust and gives a good indication of the strength of structure when compared to alternate strategies; moreover, we show that the computational cost of the proposed structural measure is reasonable.