A real-time FPGA-based radiation noise suppression and detection method with comparative analysis against deep learning techniques

The safe operation of nuclear facilities and the demand for real-time radiation monitoring have continued to increase. Active Pixel Sensor based nuclear radiation imaging technology has attracted significant attention due to its low power consumption and high integration capability. However, in high-radiation environments, APS devices are susceptible to interference from high-energy particle impacts, generating random high-amplitude noise that severely degrades video quality and reduces dose rate measurement accuracy. To address this issue, this paper proposes a real-time radiation noise suppression and dose detection method that combines time-domain minimum-value substitution with spatial median filtering with two-dimensional wavelet decomposition, implemented on a parallel FPGA architecture. The proposed method fully exploits the multi-stage pipelining and parallel processing capabilities of FPGAs to efficiently suppress radiation-induced noise in APS image streams and extract residual dose information at multiple scales. Experiments conducted using a ⁶⁰Co gamma-ray source on both a video test chart and a real-world scenario demonstrate that the method improves the peak signal-to-noise ratio by an average of approximately 11 dB after denoising, significantly outperforming Gaussian and low-pass filtering, and achieving comparable results to deep learning approaches such as DnCNN and Vision Transformer. Moreover, the hardware implementation does not require power-hungry GPUs, ensuring real-time performance for embedded applications. Further wavelet decomposition and pixel value fitting analyses confirm excellent linear correlation for dose rate estimation, with the Daubechies wavelet diagonal component achieving an R² as high as 0.99624. Overall, the proposed approach offers a low-power, high-efficiency engineering solution for real-time APS video denoising and dose detection in nuclear environments, providing a solid technical foundation for building FPGA-based intelligent nuclear radiation monitoring expert systems.