The introduction of the new edge-based spatial frequency response (e-SFR) feature, known as the slanted star, in ISO 12233:2023 marks a significant change to the standard. This feature offers four additional edge orientations compared to the previously used slanted square, enabling measurement of sagittal and tangential spatial frequency response (SFR) in addition to SFR derived from vertical and horizontal edges. However, the expanded utility provided by these additional edges presents challenges in reliably automating the placement of appropriate regions of interest (ROIs) for e-SFR analysis, thereby complicating the accurate comparison of resolution across various orientations. This paper addresses these challenges by providing recommendations for the efficient and precise detection and analysis of the ISO 12233 slanted star feature. Our recommendations are based on thorough simulations and experimentally validated results obtained under diverse and challenging conditions.
The edge-based Spatial Frequency Response (e-SFR) method was first developed for evaluating camera image resolution and image sharpness. The method was described in the first version of the ISO 12233 standard. Since then, the method has been applied in a wide range of applications, including medical, security, archiving, and document processing. However, with this broad application, several of the assumptions of the method are no longer closely followed. This has led to several improvements aimed at broadening its application, for example for lenses with spatial distortion. We can think of the evaluation of image quality parameters as an estimation problem, based on the gathered data, often from digital images. In this paper, we address the mitigation of measurement error that is introduced when the analysis is applied to low-exposure (and therefore, noisy) applications and those with small analysis regions. We consider the origins of both bias and variation in the resulting SFR measurement and present practical ways to reduce them. We describe the screening of outlier edge-location values as a method for improved edge detection. This, in turn, is related to a reduction in negative bias in the resulting SFR.