Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems

• Published in: IEEE systems journal Vol. 17; no. 4; pp. 1 - 12
• Main Authors: Rahman, Ziaur, Aamir, Muhammad, Ali, Zafar, Saudagar, Abdul Khader Jilani, AlTameem, Abdullah, Muhammad, Khan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Color; Cyber-physical systems; Environmental safety; Illumination; Image color analysis; Image contrast; Image enhancement; industrial applications; industrial cyber-physical systems (ICPSs); intrusion prevention; Lighting; Lips; machine vision; Mathematical models; Safety; Visualization; White balancing
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2023.3262593

Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded and available in various visual appearances. The process of highlighting the hidden contents in night-time contrast-distorted images is complex. Earlier approaches have solved this problem from a different perspective and achieved remarkable results that are generally unsatisfactory for images with diverse illumination distortions in ICPSs. Hence, an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems. Our proposed model has four steps: 1) removal of the unnatural color cast via a white balance technique, 2) use of probability density and softplus functions to process the images with the actual color cast, 3) using an optimization algorithm to estimate the illumination and adjusting it using a nonlinear function, and 4) blending by multiscale fusion to obtain the most effective visual result. Evaluation of ten benchmark datasets using 14 quality metrics for 22 conventional and modern algorithms shows that our approach is robust, flexible, and applicable to numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, smart mobility, and transportation, especially in low-light environments.