
ISO 12233 defines the slanted-edge technique for estimating the spatial frequency response (SFR) of imaging systems. SFRMAT, originally developed by Burns, has served for over two decades as the de facto MATLAB implementation of this standard. This paper presents a fully open-source Python implementation of SFRMAT5 with equivalent functionality and numerical fidelity. The software mirrors the original MATLAB structure to simplify maintenance and coexistence, while supporting CLI, GUI, and headless operation. Validation against the MATLAB version demonstrates practical numerical equivalence (< 10−10) on real captures where valid outputs are available. The implementation enables broader adoption in research, education, and production imaging pipelines and is available at burnsdigitalimaging. com/ software/ sfrmat .

The spatial frequency response (SFR) has long been a crucial metric for evaluating imaging quality, particularly in camera performance assessment. However, the constraints of chart-based assessment limited the evaluation of natural scenes, making it challenging to evaluate resolution accurately in real-world environments. Notably, the development of the natural scene spatial frequency response (NS-SFR) has enabled resolution evaluation from natural scenes, extending its utility to diverse applications. Nevertheless, existing NS-SFR methods have been limited to two-dimensional analysis, neglecting depth-dependent behaviors such as variations in sharpness across focal planes. To address this limitation, we propose a depth-aware extension of NS-SFR, integrating depth dimension into modulation transfer function (MTF) analysis, and establish a model of the depth-MTF relationship that derives a representative MTF value for a single image’s resolution. Our approach extends conventional planar NS-SFR analysis into a 3D depth-augmented framework that accounts for depth-dependent variations in MTF. Also our results suggest that our approach enables a more resilient and informative methodology for accurate cross-sensor comparison, yielding predictions that show a reasonable correspondence with resolution tendencies observed in natural scenes, while enhancing robustness under varying illumination.