摘要
为解决雨雾天气条件下基本农田视频监控图像的退化问题,在暗通道先验(dark channel prior,DCP)去雾算法的基础上,利用拉普拉斯金字塔的区域细节重建方法,实现了大面积亮域场景下自适应去雾的改进暗通道先验算法(modified dark channel prior,MDCP),进而基于该算法提出了一种DSP嵌入式系统的前端化视频图像去雾清晰化处理方案。试验表明:MDCP算法相较于DCP算法和Retinex算法在细节强度、色调还原以及结构信息方面均表现得更为突出,综合评测指标可达0.93;处理后的视频图像对比度良好,彩色图像颜色的饱和度和真实性有效保持,轮廓对比度以及远端天空细节明显增强;MDCP算法的处理速度优势随图像尺寸增大而逐渐增大,在图像尺寸为1 280×720时,MDCP算法比DCP算法的平均处理速度提高5.9%。研究结果为雨雾天气下退化视频图像进行前端化去雾处理方案设计提供理论依据和实践指导。
Using real-time video to capture farmland digitization regulation is an important step in the protection of essential farmland, but problems exist such as a large area of bright sky background and extreme weather i.e. image degradation caused by rain or fog. Generally, we run the defogging process which is used for image processing to clarify the hazy image in the service. However, the cost is high and the process is in unreal time so that it is not suitable for the storage of video data and real-time alarm. With the innovation of computer hardware, it is possible now to defog in real-time under the haze weather. The Langley research center of the national aeronautics and space administration transplanted the algorithm which is based on Retinex algorithm to DSP（Digital signal process） enhancement system that meets the real-time requirements to deal with 256 ×256 gray-scale image. Claire Vue＇s team from Tsinghua University developed a real-time system on the i Phone 4 to defog 192 ×144 video image. Cai Zixing＇s team from Central South University put forwarded an algorithm based on the mist theory to achieve theoretical efficiency of real-time processing. In this paper, we aim at defogging the basic farmland video surveillance images in real-time. We achieved the MDCP（modified dark channel prior）algorithm which was improved on the basis of dark channel prior defogging algorithm with the combination of the dividing and merging of human visual perception to hazing. We built up a system which can clarify the basic farmland video surveillance image by using the subsample of transmittance, adjacent pixels completion, application processing block and the front-end hardware layered method to defog. In order to objectively demonstrate the effectiveness of MDCP algorithm,we used no reference evaluation model to evaluate MDCP algorithm, dark channel prior algorithm and multi-scale Rentinex algorithm and we gained the objective assessment of clarifying the hazy image. The data showed that MDCP algorithm was more
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第10期143-148,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
国土资源部公益性行业科研专项项目(201411019)
关键词
算法
图像处理
监测
雨雾天气
去雾
algorithms
image processing
monitoring
foggy weather
defogging
