Abstract
Generally, Satellite road images are in noisy conditions such as noise, snow, thin cloud, dust, etc., which results in contrast degradations in the image. Denoising is defined as the technique for removing noise or atmospheric impurities from an image to increase the quality of an image. But most of the state of art approaches failed to remove the atmospheric effects and noise from the road image perfectly. To solve this problem, this paper majorly focuses on developing a Gray World Optimization (GWO) algorithm for the perfect estimation of atmospheric road light. The work also develops the novel method for dark channel prior-based transmission map estimation and refinement in pixel-wise and patch-wise manner. Thus, the atmospheric road effects are resolved in each pixel-based patch. Finally, a fast iterative domain guided image filtering (ID-GIF) approach was developed to obtain smoothen output with Denoising properties. The simulation results show that the proposed work provides better quantitative and qualitative results than state-of-the-art approaches.
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