Haze is an atmospheric phenomenon which diminishes visibility in outdoor images. Algorithms based on dark channel prior (DCP) and haze line prior are found to be effective for dehazing images. These two methods make use of the Laplacian matrix, which is computationally complex, memory intensive and slow, thus making it impossible to use them on high-resolution (large) images. Multiple strategies have been suggested in the literature to speed up dehazing process by avoiding the Laplacian matrix, but these methods compromise on the quality of dehazing. We propose an effective modular method which divides the input image into blocks and processes each block independently. This makes it possible to use our method for dehazing large images retaining Laplacian matting and thus ensuring the output image quality. This division results in the possibility of assuming local values of atmospheric light. We show that this approach results in better dehazing in the local regions. The effectiveness of the proposed modular architecture is tested also on a learning based method. The output of the modular method is compared with those of different state-of-the-art dehazing methods for multiple quality parameters. Toward this, we have created a dataset of hazy natural outdoor images of large size.

雾霾是一种大气现象，会降低户外图像的可见度。发现基于暗通道先验（DCP）和霾线先验的算法对图像进行除雾有效。这两种方法都使用拉普拉斯矩阵，该矩阵计算量大，内存密集且速度慢，因此无法在高分辨率（大）图像上使用它们。文献中已经提出了多种策略来通过避免拉普拉斯矩阵来加速除雾过程，但是这些方法会影响除雾的质量。我们提出了一种有效的模块化方法，该方法将输入图像分为多个块，并分别处理每个块。这使得可以使用我们的方法对保留拉普拉斯遮罩的大图像进行除雾，从而确保输出图像的质量。这种划分导致有可能假设大气光的局部值。我们表明，这种方法可以在当地改善除雾效果。所提出的模块化体系结构的有效性也在基于学习的方法上进行了测试。对于多种质量参数，将模块化方法的输出与不同去雾方法的输出进行比较。为此，我们创建了一个大尺寸的朦胧自然户外图像数据集。