Ultrasonic detection and characterization of flaws in coarse-grained materials exhibiting heterogeneous and scattering microstructure is of particular importance across many industries, but remains challenging. Most spectral based denoising methods in the literature are sensitive to material properties, which necessitate a troublesome parameter optimization process and consequently impede their application into ultrasonic image processing. In order to improve flaw visibility in an image, we propose a novel and robust clutter suppression method through spectral distribution similarity analysis (SDSA). This method isometrically segments all the time-series data in a dataset acquired by the Full-Matrix-Capture technique and then censuses the spectral distribution of global segments and of local segments for every focusing point in the Total-FocusingMethod image. The coefficient computed by measuring the similarity between the two spectral distributions reveals the possibility of a legitimate flaw indication. Experiments on two highly scattering samples were conducted to validate this method. By applying SDSA, crack visibility is greatly enhanced with an average >20 dB target-to-noise ratio enhancement for a stainless steel weld sample, whilst ~30dB improvement for an austenitic steel sample. The proposed technique retains excellent performance for both samples when the selected segment length is varied, proving its robustness and highlighting its potential for application across various materials.
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