The Deep-FIR team has recently published a paper on . In we presented a new mechanism that integrates additional information (metadata) describing the degradation process (such as the blur kernel applied, compression level, etc.) to guide the neural network to super-resolve low-resolution images with higher fidelity to the original source. This proposed meta-attention module can be integrated within existing super-resolution techniques to exploit the information available in relevant degradation parameters to improve its performance. This approach was found to improve the performance by 0.3 dB in terms of PSNR. The coding framework used for this paper is available at .

The post Published work on IEEE Signal Processing Letters first appeared on Deep-FIR.

The Deep-FIR was featured as a news item on TVM where we have described the preliminary results obtained by this project. A link to the news article and video can be found .

The post Deep-FIR on the news first appeared on Deep-FIR.

Our 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. More information about the press relase can be found here.

The post Press Release: Restoration of CCTV quality face images first appeared on Deep-FIR.

We have presented a short video during the Science in the City event that took place between the 24 and 25 of September 2020.

The post Deep-FIR took part of Science in the City first appeared on Deep-FIR.

Dr. Ing. Reuben Farrugia and Dr. Ing. Gianluca Valentino from the Department of Communications and Computer Engineering – University of Malta and members of the Data Science Research Group together with researchers on the Deep-FIR and SAT-FIRE project (Dr. Christian Galea and Dr. David Lloyd respectively), reached out to secondary students from De La Salle College, St. Joseph (Paola) and St. Michael as part of the Career’s day organized at De La Salle College. The students had the opportunity to learn more about data science and how it is used to improve the quality of CCTV videos and Satellite image.

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The Deep-FIR project was presented during the Meet the Researcher event during Science in the City which took place in Valletta. This event allows the general public to meet researchers and have a chance to disseminate our ongoing work in a fund, interactive environment.

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