A usual problem encountered during bad weather conditions is the degraded image quality due to haze/fog. In basic Gamma correction method there is always an uncertainty regarding the choice of a particular exponential factor, which improves the quality of the input image because of the nonlinearity involved in the process. This issue has been solved in this study by proposing a modified Gamma correction method, in which the exponential correction factor is varied incrementally to generate images. We also propose the implementation of an automatic image selection criterion for fusion which helps chose images with varied and distinct features. The implementation of the multi-exposure fusion framework is done in the hue-saturation-value color space which has close resemblance with the human vision. The intensity channel of the selected images is fused in the gradient domain which captures minute details and takes an edge as compared to other conventional fusion based methods. The fused saturation channel is obtained by averaging fusion followed by enhancement using a non-linear sigmoid function. The hue channel of the input hazy image is left unprocessed to avoid color distortion. The experimental analysis demonstrates that the proposed method outperforms most of the single image dehazing methods.

在恶劣天气条件下遇到的常见问题是由于雾/雾而导致的图像质量下降。在基本的伽玛校正方法中，对于特定的指数因子的选择始终存在不确定性，由于该过程涉及非线性，因此可以提高输入图像的质量。在本研究中，通过提出一种改进的Gamma校正方法解决了该问题，在该方法中，指数校正因子逐渐变化以生成图像。我们还提出了一种用于融合的自动图像选择标准，该标准可帮助选择具有不同特征的图像。多重曝光融合框架的实现是在与人类视觉极为相似的色相饱和度值色彩空间中完成的。与其他传统的基于融合的方法相比，所选图像的强度通道在梯度域中融合，该捕获微小的细节并占据边缘。融合的饱和通道是通过对融合进行平均后再使用非线性S型函数进行增强而获得的。输入模糊图像的色相通道未经处理，以避免颜色失真。实验分析表明，该方法优于大多数的单图像去雾方法。