High-speed imaging is critical for testing moving objects. However, traditional imaging methods based on charge-coupled devices or complementary metal-oxide-semiconductor devices have limitations in imaging speed and photon sensitivity. High-speed cameras face challenges such as high equipment costs, imaging difficulties in complex environments with large fields of view, and the need to balance temporal and spatial resolution. This paper proposes a Frame Interpolation High-Speed Imaging Method (FIHSIM) based on wide-field general-purpose cameras to address the abovementioned constraints. This method serves as a complementary solution to meet the needs of high-speed imaging.A virtual frame interpolation super-resolution algorithm for general camera image acquisition was proposed for FIHSIM (Fig.1). This method first preprocesses the blurred images captured by conventional general-purpose cameras to eliminate complex backgrounds. Subsequently, normalization, inverse normalization, threshold processing, and deconvolution operations are applied to map the grayscale values from the blurred images to the time domain, separating grayscale values and time information to extract precise temporal information. Finally, with the exposure time and subsampling time as input parameters, they are fed into the frame interpolation reconstruction algorithm to reconstruct the transient scene changes of dynamic targets during exposure.To validate the feasibility of the method, a comparative experiment was conducted between FIHSIM and a high-speed camera. Detailed measurements and analysis of the object's displacement and associated errors in the images indicate minimal discrepancies (Tab.1). The experimental results demonstrate that the method can accurately reconstruct the position of the object's motion front (Fig.4). In the experiment, we utilized a standard CMOS camera with a frame rate of 100 Hz and achieved an imaging speed of 12800 Hz using FIHSIM, representing a 128-fold increase. Two key camera factors constraining the ability to increase frame rates are exposure time and bit depth. The frame interpolation high-speed imaging method proposed maintains a high frame rate during continuous recording, enabling virtual frames to span multiple exposure periods. This method is suitable for scenarios requiring continuous recording of dynamic events. However, specific techniques can increase frame rates in application scenarios where continuous recording is unnecessary. The enhancement factor β, representing the multiplier for increasing the virtual frame rate, is defined by the characteristics of the camera sensor. However, in theory, it can be improved by reducing the exposure time τ to raise the maximum frame rate F_MAX, thereby enhancing the camera's ability to freeze motion.FIHSIM can accurately recover the precise positions of objects at different time points during exposure from the motion-blurred areas in the images, thereby significantly enhancing the temporal resolution of the images. This technology effectively overcomes the limitations of traditional CMOS cameras in high-speed imaging. Furthermore, by precisely measuring the displacement of dynamic target objects, we experimentally validated the outstanding performance of FIHSIM in high-speed imaging. This method utilizes widely available portable general-purpose cameras as imaging and detection platforms, simplifying the implementation of the imaging process. Additionally, FIHSIM exhibits excellent scalability, allowing for selecting cameras with different fields of view and spatial resolutions and pulse light sources with varying pulse widths based on experimental requirements to capture vast fields of view and objects moving at different speeds.