This paper introduces a reconfigurable AI-enabled scalable median filter with an adaptive impulse detector designed for FPGA-based real-time imaging systems. Its primary objective is to address the degradation of image quality caused by mixed impulsive noise during real-time image transmission and reception. Existing median filters often struggle to provide real-time image processing results that meet high standards in terms of both accuracy and speed. This approach effectively suppresses noise in real-time images while preserving essential edge details, which are crucial for the performance of real-time imaging systems. The algorithm introduces a novel technique of replacing noisy pixels with the processed central value within the image filtering window. This ensures fidelity to the original pixel, which is vital for applications such as image filter cores. To handle high noise densities in real-time systems, the methodology employs a scalable sorting approach for median filtering and an impulse detector, ensuring robust noise reduction without excessive computational complexity. The AI-enabled scalable median filter system achieves a significant reduction in dynamic power consumption, realizing an impressive 46% decrease in power consumption and an 82% reduction in area compared to the existing system. This is particularly beneficial for addressing resource and power-aware constraints in real-time systems. Comprehensive performance evaluation, including metrics such as PSNR, MSE, IEF, and SSIM, demonstrates the efficacy of the filter in enhancing image quality, a critical factor for the success of real-time imaging systems.

摘要

本文介绍了一种可重构、支持 AI 的可扩展中值滤波器，具有专为基于 FPGA 的实时成像系统设计的自适应脉冲检测器。其主要目标是解决实时图像传输和接收过程中混合脉冲噪声引起的图像质量下降问题。现有的中值滤波器通常难以提供在精度和速度方面满足高标准的实时图像处理结果。这种方法有效地抑制了实时图像中的噪声，同时保留了基本的边缘细节，这对于实时成像系统的性能至关重要。该算法引入了一种用图像滤波窗口内处理后的中心值替换噪声像素的新技术。这确保了原始像素的保真度，这对于图像滤波器核心等应用至关重要。为了处理实时系统中的高噪声密度，该方法采用可扩展的排序方法进行中值滤波和脉冲检测器，确保稳健的噪声降低，而无需过多的计算复杂性。支持AI的可扩展中值滤波器系统实现了动态功耗的显着降低，与现有系统相比，功耗显着降低了46%，面积减少了82%。这对于解决实时系统中的资源和功率感知约束特别有益。全面的性能评估，包括 PSNR、MSE、IEF 和 SSIM 等指标，证明了滤波器在提高图像质量方面的功效，而图像质量是实时成像系统成功的关键因素。