A privacy-preserving protocol for efficient nighttime haze removal using cloud based automatic reference image selection and color transfer as a service

Abstract

The advanced Internet technologies have migrated the people to rejoice a virtual environment known as cloud computing. The user can avail the desired services on a pay-as-you-go model, without worrying about the burden of infrastructure maintenance. However, privacy is one of the major issues in cloud computing. This issue is further widened for highly confidential multimedia data like surveillance images and videos. In the context of cloud based smart multimedia systems, it has been found that due to inconsistent weather conditions, there is a usual requirement of post-processing the captured multimedia for better appearance. However, privacy related concerns are resisting users to move their data to the cloud. One such problem is addressed in this paper, specializing the task of efficient nighttime haze removal using privacy-preserving cloud based automatic reference image selection and color transfer as a service. Different from daytime conditions, nighttime haze image consists of multiple light sources, which makes an ambiguous situation for haze removal. We address this problem by first selecting an appropriate gray image as the reference and then transferring its colors to nighttime haze image. This makes the transformed image a suitable candidate for radiance recovery. The proposed protocol is designed to securely outsource this considerable burden from user end. We accomplish this by first proposing an automatic reference gray image selection method, followed by efficient handling mechanisms for technical challenges arising due to performing color transfer operations securely over cloud. Experimental results and validation demonstrates superiority of the proposed method over state-of-the-art schemes. Security analysis of the proposed protocol is established through a challenge-response game model.