Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor
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摘要: 雾天图像增强具有重要现实意义。由于现有的图像去雾算法在提升图像全局对比度的能力上存在不足，为此将暗通道先验去雾算法与直方图均衡化算法的各自优势进行整合，该文提出一种雾天图像增强新算法。首先，分别采用基于导向滤波的暗通道先验去雾算法和基于HSV色彩空间的直方图均衡化算法处理雾天图像；然后，基于修正的透射率图构造权值因子，将上述两种处理结果加权融合，得到输出图像。仿真实验结果表明，该算法比现有去雾算法具有更高的标准差、平均梯度与信息熵，具有更好的全局与局部对比度增强效果。算法运行时间主要依赖于图像去雾环节，处理一般尺寸图像能够满足实时要求。Abstract: Hazy image enhancement has important practical significance. Since the existing haze removal algorithms have disadvantages in improving the global contrast of images, a novel hazy image enhancement algorithm is presented by integrating advantages of haze removal and histogram equalization. Firstly, the hazy image is processed respectively using guided filtering-based dark channel prior algorithm and HSV space-based histogram equalization algorithm. Then, the output image is obtained by fusing the above two results using weighting factor which is constructed by the revised transmittance map. Simulation results show that the algorithm has higher standard deviation, average gradient and information entropy than the present hazy removal algorithm, and shows better result of global and local contrast. The running time of the algorithm mainly depends on the process of haze removal, which can meet the real-time requirements for normal size image.
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Key words:
- Image fusion /
- Hazy image /
- Transmittance /
- Dark channel prior /
- Histogram equalization
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表 1 不同增强算法的客观评价
图像 增强算法 标准差 平均梯度 信息熵 亮度 图像1 输入图像 19.568 1.505 6.015 88.556 文献[15]算法 23.001 3.039 6.343 42.771 HSV空间HE 75.139 5.937 7.515 127.243 本文算法 111.206 9.375 7.314 82.391 图像2 输入图像 53.585 7.464 7.627 108.122 文献[15]算法 44.020 7.164 7.085 71.431 HSV空间HE 73.494 12.531 7.759 127.331 本文算法 110.384 12.990 7.538 95.696 图像3 输入图像 58.324 6.359 7.749 103.088 文献[15]算法 44.392 6.788 7.149 65.680 HSV空间HE 74.148 8.584 7.877 127.448 本文算法 111.854 11.428 7.620 93.043 图像4 输入图像 75.896 9.661 7.449 119.459 文献[15]算法 63.888 10.246 7.488 59.750 HSV空间HE 76.886 9.767 7.460 127.167 本文算法 129.088 12.360 7.582 80.07 平均 输入图像 51.843 6.247 7.21 104.806 文献[15]算法 43.825 6.809 7.0162 59.908 HSV空间HE 74.916 9.204 7.652 127.297 本文算法 115.633 11.538 7.513 87.8 
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