Several countries around the world use closed-circuit television (CCTV) videos as forensic evidence to combat crime. These cameras cover large fields of view, where low-resolution facial images are typically captured, making the identification of the subject of interest very difficult. A group of researchers at the University of Malta is conducting research with the aim of improving the quality of facial images captured by typical CCTV cameras. The team has adopted an advanced artificial intelligence (AI) technique based on deep-learning to restore very low-resolution and compressed images. The developed algorithm has shown that it is capable of restoring degradations that are typically present in CCTV footage. The figure shows a number of low-quality facial images on the left and the restored images on the right. By comparing the restored face to the actual high-resolution face, one can see that the proposed method is able to significantly improve the quality of the face while preserving the identity of the person of interest.

This approach was tested in a controlled environment and has shown promising results. Nevertheless, this is just a first step in trying to improve the quality of facial images captured by real-world CCTV systems, since different CCTV cameras will produce a variety of different distortion patterns. Our team is now working on improving the performance of the existing algorithm and extending it to generalize to the different degradations produced by different CCTV cameras. Moreover, the correlation present in different video frames will be exploited to develop a multi-frame face super-resolution algorithm.

The Deep-FIR project is financed by the Malta Council for Science & Technology (MCST), for and on behalf of the Foundation for Science and Technology, through the FUSION: R&I Technology Development Programme. This project is a collaboration between the Department of Communications and Computer Engineering, the Department of Computer and Information Systems and the Department of Systems and Control Engineering at the University of Malta. Ascent Software, a local premier software development house, will be developing the software package. More information about this project can be found on the project website.

Source: https://www.um.edu.mt/projects/deep-fir/

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