A Fast Sand Dust Image Enhancement Algorithm and Number Plate Detection

Abstract

Abstract: The color variation and limited visibility of images taken in sand-dust conditions have a significant negative impact on computer vision systems. We suggest a quick and efficient approach to improve photographs taken in sand-dust weather in order to address the aforementioned issues. First, we make amends for the blue channel's loss value. The color of the image that has been damaged by sand dust is then corrected using white balance technology. In order to improve the image contrast and edge accuracy, guided image filtering is utilized, and an adaptive approach is used to determine the detail layer's magnification factor in order to improve the image detail information. We also added an additional feature of number plate detection of the sandy images.The aim of this task is to develop an automated system that can accurately and efficiently detect and recognize number plates from sandy images captured by a camera. This technology has various applications, such as traffic control, toll collection, parking management, and law enforcement. The experimental results demonstrate that the approach can successfully restore the fading characteristics of a large number of sand-dust-damaged photos. The proposed method may considerably improve the photos acquired during sand-dust weather conditions, and the results are better than those of existing methods, according to experimental results via qualitative and quantitative assessments