针对图像去雾算法中存在因介质透射率估计不准确而造成色彩失真、去雾不完全的问题, 提出了一种改进残差神经网络的图像去雾算法. 首先采用并行多尺度卷积层提取雾图像特征. 然后通过引入了深度可分离卷积层的残差网络学习介质透射率, 并利用加权引导滤波细化介质透射率. 最后根据大气散射模型反演得到无雾清晰图像. 实验结果表明, 该算法与其他去雾算法相比在峰值信噪比(peak signal to noise ratio, PSNR)和结构相似度(structural similarity, SSIM)指标上取得了一定的提高, 并且去雾图像在主观视觉上也取得了较好表现.
For color distortion and incomplete dehazing caused by inaccurate media transmittance in the image dehazing algorithm, an image dehazing algorithm with an improved residual neural network is proposed. First, a parallel multi-scale convolutional layer is adopted to extract the characteristics of the haze image. Then the media transmittance is learned by introducing the residual network of the depthwise separable convolutional layer and refined by the weighted guided filter. Finally, according to the atmospheric scattering model, a clear image without hazy is obtained. Experimental results show that compared with other dehazing algorithms, the proposed algorithm improves peak signal to noise ratio (PSNR) and structural similarity (SSIM) indicators, and the dehazing image also performs well in subjective vision.