Image dehazing has become a critical problem to cater to as it has several parameters that need to be addressed. Real color, contrast, and illumination are the major parameters that are to be restored in the dehazed image. Different scenarios distort these parameters in different ways so it is difficult to restore the original image. Many approaches are used in the literature to cater to these problems but suffer from low contrast, faded color, and weak edges. This article introduces an effective technique, which is named as dark channel spatial stimuli gradient model (DCSSGM) that performs well for the aforementioned problems. The DCSSGM technique applies the dark channel prior (DCP) and spatial stimuli gradient sketch model (SSGSM) on each color channel to eliminate the haze from the image and to restore true edges. SSGSM is responsible to restore robust edges in an image using the perceived brightness and calculations based on neighborhood similarity. Morphology is applied to the resultant image to receive sharp true edges. The final restored image is the output of dynamic histogram equalization (DHE) which restores the contrast of the image. The evaluation analysis qualitatively and quantitatively concludes that the DCSSGM technique outperforms other state-of-the-art image dehazing techniques.

由于图像去雾具有几个需要解决的参数，因此图像去雾已成为解决该问题的关键问题。真实的颜色，对比度和照明度是要在除雾后的图像中恢复的主要参数。不同的场景以不同的方式扭曲了这些参数，因此很难还原原始图像。文献中使用了许多方法来解决这些问题，但是存在对比度低，颜色褪色和边缘薄弱的问题。本文介绍了一种有效的技术，该技术被称为暗通道空间刺激梯度模型（DCSSGM），可以很好地解决上述问题。DCSSGM技术在每个颜色通道上应用了暗通道先验（DCP）和空间刺激梯度素描模型（SSGSM），以消除图像中的雾度并恢复真实边缘。SSGSM负责使用感知的亮度和基于邻域相似性的计算来还原图像中的鲁棒边缘。将形态学应用于所得图像以接收清晰的真实边缘。最终还原的图像是动态直方图均衡化（DHE）的输出，该图像可还原图像的对比度。评估分析定性和定量地得出结论，DCSSGM技术优于其他最新图像去雾技术。