结合图像纹理的自适应透射率修正去雾算法

Adaptive Transmissivity Correction Algorithm for Defogging Combining Image Texture Information

图像去雾算法在户外智能监控及交通导航等领域普遍应用,通过去雾后提高图像的清晰度以提高目标的识别效果。暗通道及其改进算法在天空等景深灰色较亮区域的透射率估计存在偏差,易导致图像失真、细节模糊等问题,对智能交通领域图像识别有严重影响。本文提出了自适应透射率去雾方法对透射率进行补偿,采用对数变换获得对数补偿算子调节景深区域透射率,根据图像信息丰富程度计算出暗通道的置信度,结合图像纹理信息构造出纹理补偿算子,通过自适应透射率补偿参数调整灰色亮部区域的初始透射率,可有效改善图像去雾后失真等问题。本算法的平均梯度、信噪比、信息熵等客观指标较其他去雾算法均有所提高,图像中灰色亮部区域透射率补偿效果良好,复原图像细节清晰自然、亮度适中,有效提升了图像质量。

Image defogging algorithm is widely used in outdoor intelligent monitoring and traffic navigation fields.After defogging,the image clarity is improved to enhance the recognition effect of the target.Dark channel and its improved algorithm have errors in transmittance estimation in bright gray areas such as sky,and are prone to distortion and blurred image details,which will affect image recognition in intelligent transportation field.An adaptive transmittance defogging method is proposed to compensate the transmissivity.Logarithmic transformation is used to obtain logarithmic compensation operator to adjust the transmissivity in the depth of field area.The confidence of dark channel is calculated according to the richness of image information,and the texture compensation operator is constructed combining the image texture information.It can effectively improve the image distortion after defogging.Compared with other defogging algorithms,the proposed algorithm has improved the average gradient,signal-to-noise ratio(SNR),information entropy and other objective indicators.The image quality has been effectively improved with good transmission compensation effect for the gray bright area,clear and natural image details and moderate brightness.