基于FPGA的视频图像去雾算法的优化与实现  

Optimization and implementation of image defogging algorithm based on FPGA

作　　者：郝振中 余耀 孙静[1] Hao Zhenzhong;Yu Yao;Sun Jing(School of Electronic Engineering/School of Intelligent Manufacturing,Anhui Xinhua University,Hefei 230088,China;School of Electronics and Information Engineering,Wuxi University,Wuxi 214063,China)

机构地区：[1]安徽新华学院电子工程学院/智能制造学院,安徽合肥230088 [2]无锡学院电子与信息工程学院,江苏无锡214063

出　　处：《电子技术应用》2024年第5期90-96,共7页Application of Electronic Technique

基　　金：安徽省教育厅重点科研项目(KJ2021A1162);安徽省教育厅质量工程项目(2021sx062)。

摘　　要：在恶劣天气条件下采集的图像存在对比度差、清晰度下降等问题。图像质量的恶化制约着计算机视觉的准确性和自动化任务的效率。给出了一种基于限制对比度自适应直方图均衡(Contrast Limited Adaptive Histogram Equalization,CLAHE)与改进多尺度Retinex(Multi-Scale retinex,MSR)的图像去雾算法。该算法将输入的含雾降质图像先经过CLAHE算法处理,再用MSR算法处理,对图像MSR算法处理时,引入Gamma校正因子估计入射光,并对算法中的环绕函数进行优化。结果表明,所提出算法处理后的图像相比原图,图像的信息熵、平均梯度和标准差等方面均有提升;并设计硬件电路,成功在FPGA上演示了视频实时去雾,提高了视频图像去雾的实时性。对板级资源与功能消耗进行了数字化的分析,证明所设计硬件系统属于低功耗范畴。Images collected under severe weather conditions have problems such as poor contrast and reduced clarity.The deterioration of image quality limits the accuracy of computer vision and the efficiency of automated tasks.This article proposes an image dehazing algorithm based on contrast limited adaptive histogram equalization(CLHE)and improved multi-scale Retinex(MSR).In this algorithm,the input foggy degraded image is first processed by the CLAHE algorithm and then the MSR algorithm.When processing the image with the MSR algorithm,the Gamma correction factor is introduced to estimate the incident light and the surround function in the algorithm is optimized.The results show that compared with the original image,the image processed by this algorithm has improved the information entropy,average gradient and standard deviation of the image.The hardware circuit was designed and the video real-time dehazing was successfully demonstrated on FPGA,which improved the quality of the video image.A digital analysis of board-level resources and function consumption was conducted,proving that the hardware system in this article belongs to the low-power category.

关 键 词：图像质量 CLAHE 多尺度RETINEX FPGA 视频去雾 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]