Analysis of noise removal speed and accuracy in various color spaces of image

Images taken outdoors are highly likely to generate noise due to rain, snow, and fog. So, removing noise is one of the important fields in image processing. This field usually requires a real-time, high-speed image processing. The noise removal field could be used in the pre-processing stage of extensive image processing such as perception processing of autonomous vehicles. Therefore, optimization for real-time processing should be preceded and an approach that characteristics of color space will be one of them. This research applies several color spaces to image processing and analyzes them through three steps. First, the dataset construction. A ground truth and a noised dataset are needed for quantitative evaluation. Second, image de-noise. Third, evaluation through indicators. Through this, analyze the possibility of optimizing image processing through color space.