基于模糊理论和CLAHE的雾天图像自适应清晰化算法
An Adaptive Foggy Image Enhancement Algorithm Based on Fuzzy Theory and CLAHE
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摘要: 为了解决雾天图像低对比度的问题, 提出了一种基于模糊理论和CLAHE的雾天图像的自适应清晰化算法.此算法结合图像的均值和标准差, 将雾天图像从空域转换到模糊域, 采用模糊增强算法实现全局雾天图像的自适应对比度增强后再采用有约束的局部直方图算法对雾天图像的亮度分量进行处理, 在空域内进一步实现雾天图像的对比度增强.实验结果表明, 该算法将模糊域和空间域的方法相结合, 可以提高雾天图像的亮度和对比度, 使雾天图像的视觉效果得到一定改善.Abstract: In order to enhance the contrast of foggy images, a new self-adaptive algorithm for foggy image based on the fuzzy theory and CLAHE was proposed.The algorithm first improve the contrast of foggy images adaptively in the fuzzy region by use of the mean and standard deviation, and then improve the luminance and contrast by use of contrast-limited adaptive histogram equalization on the value of images.Experimental results demonstrate that the proposed algorithm can effectively improve the visual effect of foggy images by combining the methods of fuzzy region and spatial domain.
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