摘要： In this paper we propose a new algorithm for real-time ?ltering of video sequences corrupted by Poisson noise. The algorithm provides effective denoising (in some cases overcoming the ?ltering performances of state-of-the-art techniques), is ideally suited for hardware implementation, and can be implemented on a small ?eld-programmable gate array using limited hardware resources. The paper describes the proposed algorithm, using X-ray ?uoroscopy as a case study. We use IIR ?lters for time ?ltering, which largely simpli?es hardware cost with respect to previous FIR ?lter-based implementations. A conditional reset is implemented in the IIR ?lter, to minimize motion blur, with the help of an adaptive thresholding approach. Spatial ?ltering performs a conditional mean to further reduce noise and to remove isolated noisy pixels. IIR ?lter hardware implementation is optimized by using a novel technique, based on Steiglitz–McBride iterative method, to calculate ?xed-point ?lter coef?cients with minimal number of nonzero elements. Implementation results using the smallest StratixIV FPGA show that the system uses only, at most, the 22% of the resources of the device, while performing real-time ?ltering of 1024 × 1024@49fps video stream. For comparison, a previous FIR ?lter-based implementation, on the same FPGA, in the same conditions and constraints (1024 × 1024@49fps), requires the 80% of the logic resources of the FPGA.