Image De-Weathering Using Median Channel Technique and RGB-based Transmission Map for Autonomous Vehicles

Static weather conditions like fog, haze, and mist in hilly and urban areas cause reduced road visibility. Due to different weather conditions, autonomous vehicles cannot identify objects, traffic signs, and signals. So, this leads to many accidents, endangering living beings’ lives. The significance of this work lies in its aim to develop a model that can provide clear visibility for autonomous vehicles during bad weather conditions. Image restoration is one of the important issues in the image processing field as the images may be of low contrast and quality due to restricted visibility and, the development of a model that reduces the halos and artifacts produced in the image using the Median Channel based Image Restoration (MCIR) technique has significant research value. In this technique, the image restoration is done by calculating the atmospheric light and the transmission map using the MCIR technique and patching the pixels for different patch sizes. The Dark Channel Prior (DCP) method and the MCIR technique are compared for different patch sizes by evaluating the output images using the PSNR, SSIM, and MSE metrics. The results show that MCIR technique provides better Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) values than the DCP method with reduced halos and artifacts. This result highlights the effectiveness of the MCIR technique for image restoration. The software model developed can be applied to autonomous vehicles and surveillance cameras for the restoration of the images, which can improve their performance and safety.