ABSTRACT

Image captured in bad weather conditions confines scene prominence, appears grey and diminishes image contrast. This usually happens due to atmospheric dispersing phenomenon that affects the quality of outdoor computer vision frameworks. This deprivation relies on the gap between the object point and the camera and mostly differs for every pixel present in an image. Transmission coefficients, which state the aforementioned dependence are used to manage the haze level in each pixel. Our algorithm is subject to the presumption that the haze-free image forms clusters given in RGB space. The pixels over the image plane are often found at different locations and their distance from camera also differs. These fluctuating distances give rise to different transmission coefficients. Consequently, these colour clusters form the certain lines of colours in RGB space known as haze lines. The first step is to assure accurate estimation of the atmospheric light, for this an additional wavelet channel is recommended, based on frequency subdivision. The next step is to separate the average gradients present in the foggy regions of an image according to the frequency criteria. Lastly, the haze-free image information is retrieved by utilising the atmospheric scattering model on low and high frequencies according to the edges of unpredicted change in the field depth. Using the non-local and frequency information retrieval, proposed algorithm recovers the haze-free image, efficiently.

摘要

在恶劣天气条件下拍摄的图像限制了场景突出，显得灰暗并降低了图像对比度。这通常是由于影响室外计算机视觉框架质量的大气扩散现象而发生的。这种剥夺依赖于物点和相机之间的间隙，并且对于图像中存在的每个像素来说大多不同。表示上述相关性的透射系数用于管理每个像素中的雾度水平。我们的算法假设无雾图像形成在 RGB 空间中给出的簇。图像平面上的像素通常位于不同的位置，它们与相机的距离也不同。这些波动的距离导致不同的传输系数。最后，这些颜色簇形成了 RGB 空间中的某些颜色线，称为雾线。第一步是确保对大气光的准确估计，为此建议使用基于频率细分的附加小波通道。下一步是根据频率标准分离图像模糊区域中存在的平均梯度。最后，根据场深的不可预测变化的边缘，利用低频和高频的大气散射模型来检索无雾图像信息。使用非局部和频率信息检索，该算法有效地恢复了无雾图像。基于频率细分。下一步是根据频率标准分离图像模糊区域中存在的平均梯度。最后，根据场深的不可预测变化的边缘，利用低频和高频的大气散射模型来检索无雾图像信息。使用非局部和频率信息检索，该算法有效地恢复了无雾图像。基于频率细分。下一步是根据频率标准分离图像模糊区域中存在的平均梯度。最后，根据场深的不可预测变化的边缘，利用低频和高频的大气散射模型来检索无雾图像信息。使用非局部和频率信息检索，该算法有效地恢复了无雾图像。