Multi-scale contrast-to-noise ratio (MS-CNR): a novel metric for quantitative defect characterisation without manual region specification

In numerous research domains where imaging plays a pivotal role in analysing specific objects or processes, it is crucial to quantitatively evaluate the performance of acquisition systems and processing algorithms in differentiating the target from its background. This paper presents the Multi-Scale Contrast-to-Noise Ratio (MS-CNR) metric, a novel tool for precise defect quantification across various imaging modalities. The MS-CNR metric employs the Laplacian of Gaussian (LoG) operator to analyse contrast at multiple scales, allowing for effective quantitative defect characterisation without relying on predefined regions for defects or noise. Through comprehensive evaluation with synthetic and real data, the MS-CNR metric demonstrates a strong correlation with human visual perception and other well-established SNR metrics. It provides consistent and reproducible results, outperforming traditional SNR metrics that may be affected by specific types of noise. The MS-CNR metric’s robust performance and alignment with visual assessments make it a valuable addition to imaging analysis, offering a reliable and automated approach for evaluating defect visibility.