Noise Image Processing Algorithm for a Low- light EBCMOS Acquisition System Based on FPGA

The development of Electron-Bombarded Complementary Metal-Oxide-Semiconductor (EBCMOS) technology marks a significant advancement in image sensors, particularly enhancing low- light and night vision applications. This paper addresses challenges in device development due to material and manufacturing imperfections that introduce complex noise into night vision imagery. Additionally, it highlights the drawbacks of traditional software-based image processing platforms, including their low real-time performance and high operational costs. An analysis of low-light imaging characteristics of EBCMOS, developed by the Xi'an Institute of Optics and Mechanics, is presented. This research introduces a multistage pulse noise suppression and edge enhancement algorithm, designed on a Xilinx-FPGA platform, tailored to tackle mixed noises such as Poisson noise, salt-and-pepper noise, and speckle noise prevalent in low-light conditions. EBCMOS sensors are distinctive in their ability to enhance visibility in dark environments through an electron-bombarding mechanism that amplifies signals before CMOS processing. This capability is crucial for applications requiring high-quality night vision, such as security surveillance and wildlife monitoring. The imperfections in materials and fabrication processes can result in various noise types, degrading image quality and obscuring critical details. The paper details a sophisticated FPGA-based algorithm that leverages modern processing power to efficiently reduce noise while preserving important image details. This is achieved through advanced noise reduction techniques that specifically target the unique characteristics of each noise type, improving upon traditional methods like median filtering and Gaussian blurring. Experimental results show that this algorithm enhances the Peak Signal-to-Noise Ratio (PSNR) by 11.37 % over median filtering and 26.64 % over Gaussian blurring. Moreover, the processing speed for a single image frame is improved approximately 21 times compared to software platforms on high- end processors, demonstrating the algorithm's capability to handle real-time image processing tasks efficiently. This not only suggests potential cost reductions but also supports the integration and miniaturization of wearable night vision devices. In summary, this study provides significant insights into the benefits of FPGA-based image processing for EBCMOS technology in night vision applications. The advancements facilitate more effective and efficient night vision systems and promote the development of integrated, lightweight wearable devices, offering substantial benefits for both military and civilian uses.