Design of adaptive integrated fast image enhancement system for general, haze, low light, back-light condition

Abstract

An input images via the camera on fog, night or back-light condition does not guarantee a good visibility. Therefore, image enhancement methods such as de-hazing, night image enhancement, back-light enhancement are very important part for video surveillance and analytics. To solve this problem, various haze-removal, night image and back-light enhancement methods have been proposed through a lot of paper. The proposed methods are effective to improve in each case, but it does not improve the image adaptively to the various conditions. In this paper, we propose method that can classify condition of input images adaptively and improve the visibility of image automatically. The proposed method classifies the input image’s condition using analysis information based on average brightness, global and local variance. Then it enhance input image on various conditions by selecting enhancement methods for each situation. Enhancement methods are applied the already proposed methods previous our papers. The proposed method was classified fog, night, back-light images to 80 percent accuracy improvement of each image. Also, proposed method is showed effective improvement results than the traditional method in subjective assessment, and through the objective evaluation it was able to confirm that suitable for real-time image processing.