彩色图像去雾清晰化研究

为了解决雾霾天气的影响下成像设备采集的图像容易出现的降质及色彩失真问题, 并从有雾图像中增强其细节信息, 提高原图像的对比度和清晰度. 将彩色图像RGB通道分别做相应的图像增强算法处理, 全局直方图均衡化把整体的灰度直方图进行均匀分布的处理, 小波变换算法对彩色图像进行多层次分解, 多尺度Retinex算法通过高斯函数做卷积运算对图像做多尺度变换. 实验结果表明, 全局直方图均衡化、小波变换算法和多尺度Retinex算法都能增强雾天图像的景物信息, 有对应于各自的优势和不足. 相比较这3种算法而言, 多尺度Retinex算法得到的去雾图像亮度增强、细节信息突出、失真度小, 能更好地进行去雾增强.

Research on Dehazing and Clarity of Color Image

This study aims to deal with the blur and color distortion of images collected by imaging equipment in hazy weather, enhance the detail information of foggy images, and improve the contrast and clarity of original images. The RGB channels of color images are processed by corresponding image enhancement algorithms. The global histogram equalization makes the whole gray histogram evenly distributed; the wavelet transform algorithm decomposes color images at multiple levels, and the multi-scale Retinex algorithm is adopted for the multi-scale transformation of images through the Gaussian convolution operation. The experimental results show that the global histogram equalization, the wavelet transform, and the multi-scale Retinex algorithm can enhance the hazy scenes in original images with their own advantages and disadvantages. Among the three algorithms, the multi-scale Retinex algorithm achieves the best dehazing and enhancement because of its enhanced brightness, prominent detail information, and slight distortion.