Defect detection based on monogenic signal processing

Using an infrared image sequence, how can one make the inner structure of a sample more visible without human supervision nor understanding of the context? This task is well known as a challenging task. One of the reasons is due to the great number of external events and factors that can influence the acquisition. This paper introduces a solution to this question. The sequence of infrared images is processed using the monogenic signal theory in order to extract the phase congruency. The Fourier Transform must respect the Hermitian property and it does thank to the Hilbert Transform in the 1D case, however this property is not respected in 2D. It does thanks to some approximation made in the analytic signal. The monogenic signal theory consists in reprocessing the Fourier Transform by replacing the Hilbert Transform by a Riesz Transform in order to maintain the Hermitian symmetry. In other words the phase congruence can be described as a feature detection approach. Using the assumption that the symmetry, or asymmetry of the phase does represent the similarity of the features at one scale, then the phase congruency represents how similar the phase values are at different scales. The proposed approach is invariant to image contrast which makes it suitable for applications. It can also give valuable results even with very noisy sequences. The proposed approach has been evaluated by using referenced Carbon Fiber Reinforced Plastic sample.