Context-based fusion and enrichment by adapted coefficients for image enhancement purposes

This paper proposes an efficient method to improve image quality based on Context-based enhancement techniques, particularly towards real-time applications in dedicated hardware systems. The main idea is that all the important information of a low-quality an image are combined with a high-quality daytime background image at the same scene to output an image with improved quality, and thus main objects of the low-quality input images can be recognized more easily. In comparison, almost all current context-based enhancement techniques have processed with complex algorithms, thereby being time-consuming and hard to perform in real-time processing when applying into high-resolution images/videos. The developed technique here outperforms these original methods, which is demonstrated by without complex formulas (such as DST and IDST), whereas not only still keeping all the important information of the low-quality nighttime input images, but also limiting problems often existing in context fusion techniques, such as ghosting, haloing and artifact effects. The experimental results demonstrate that our approach has been efficient and gained a better quality of output images in comparison with the results of the Denighting method [8].