基于通道差值模型的导向滤波去雾算法及其FPGA实现

Design and FPGA Implementation of Dehazing Based on Channel Difference Model and Guided Filtering

计算机视觉系统受到有雾天气的影响会导致捕获的图像质量较差。为了解决该问题,文中提出了一种基于通道差值模型的导向滤波去雾算法及其FPGA(Field Programmable Gate Array)设计。通过分离雾天图像的亮通道与暗通道得到通道差值模型,并将该模型作为导向滤波的引导图对雾天图像进行平滑处理,最后进行高升压滤波操作得到去雾图像,设计硬件架构并在FPGA上实现。实验结果表明,去雾后的图像场景照度均匀,纹理信息恢复程度较好且颜色保真度高,对于480×270大小的图像,电路综合频率为108.448 MHz,吞吐量为323.47 MB·s^(-1),完成整个去雾过程花费时间为0.0012 s。实验结果证明文中所提算法及其硬件设计能够有效提高图像可见度和去雾速度。

Computer vision systems are affected by foggy weather,resulting in poor quality images captured.To solve this problem,this study proposes a guided filtering dehazing algorithm based on channel difference model and its FPGA design.The channel difference model is obtained by separating the bright channel and dark channel of foggy image,and the model is used as a guide map for guided filtering to smooth the foggy image.Finally,a high boost filtering operation is performed to obtain a dehazed image.The hardware architecture is designed and implemented on FPGA.The experimental results show that the image scene after dehazing has uniform illumination,high degree of texture information recovery and high color fidelity.For an image of 480×270 size,the integrated frequency of the circuit is 108.448 MHz,the throughput is 323.47 MB·s^(-1),and the time to complete the entire dehazing is 0.0012 s.These results indicate that the proposed algorithm and its hardware design can effectively improve image visibility and dehazing speed.