###### Abstract
's [21] approach utilizes the Sobel operator and the Hough transform for edge angle estimation, examining how estimation errors affect the method's accuracy. [PERSON] [22] focused on the impact of inclination estimation errors on the stability and repeatability of MTFs obtained via this method. A critical step in the slanted-edge method's algorithm involves differentiating the ESF to derive the LSF, a process that can amplify noise. Consequently, noise impact analysis and suppression techniques have become a focal area of research. To mitigate noise's impact on accuracy, scholars have suggested fitting the ESF with a function model. [PERSON] and [PERSON] [23] and [PERSON], for instance, identified the midpoint of each data row as half the mean gray value between the dark and light regions of the slanted-edge image, constructing an oversampled ESF fitted with a Fermi function to counter noise effects. [PERSON] et al. [24] enhanced MTF detection accuracy by refining the ISO 12233 slanted-edge method with a similar Fermi function fitting of the ESF, while [PERSON] [25] achieved higher accuracy through an oversampling ESF, employing image rotation, cumulative distribution function fitting, and projection. The analysis above reveals that the MTF testing of remote sensing cameras using the slanted-edge method is significantly influenced by the chosen calculation method, with varying algorithmic models leading to considerable deviations in results [26]. In this paper, according to the working state of the space camera on orbit and the imaging characteristics, combined with the relevant theories of optoelectronic imaging systems, the optimal algorithm based on the slanted-edge optimal OSR adaptive model's slanted-edge method calculation is proposed through the study of the sub-pixel edge detection fitting algorithm and edge diffusion function extraction. The GLCM method is used to detect the sub-pixel edges and accurately locate the edge position, and then the edge points are projected, and the sub-pixel arrays are sampled, fitted, and smoothed with a variable OSR for different angles to obtain the ESF, and the differentiation of the ESF curves is used to obtain the LSF, and the smoothing of the LSF curves is performed by using the Tukey window function. Finally, the Fourier transform mode normalization is applied to the LSF curve to obtain the MTF curve. In order to verify the reasonableness of the algorithm, simulation experiments are used to verify the response of the imaging system to the spatial frequency through the mathematical function to simulate the response of the imaging system to the spatial frequency and obtain the slanted-edge image and the theoretical MTF curve, respectively, using the ISO-cos, OMNI-sine, and the algorithm proposed in this paper to calculate the MTF curve of the slanted-edge image and compare it with the theoretical MTF curve, which shows that the MTF curve of the proposed algorithm at each frequency has a higher similarity to the theoretical MTF curve, verifying the reasonableness of the algorithm in this paper. This study shows that the MTF curves at each frequency of the proposed algorithm have high similarity with the theoretical MTF curves, which verifies the reasonableness of the algorithm in this paper. Subsequently, the image factors affecting the calculation of the slanted-edge method are analyzed. It establishes the relationship between the slanted-edge angle and ROI in the MTF calculation results through simulation. The simulation results are then compared in terms of accuracy and stability to ascertain the optimal slanted-edge angle range and the ideal ROI range for the slanted-edge method. Under optimal conditions for the slanted-edge angle and image ROI, simulations are conducted to obtain images with varying contrasts and SNRs. These simulations aim to investigate the impact of these varying contrasts and SNRs on the calculation results. In the laboratory, the MTF test validation system was constructed.