Résumé:
From the face to a natural quality image Summary This master’s thesis focuses
on the development of a face image super-resolution system aimed at reconstructing
low-resolution images into high-resolution natural quality images. Using deep learning
techniques, particularly convolutional neural networks (CNN), our research has led to
a high-performing system.
Our study may have significant implications, particularly in the fields of facial
recognition, video surveillance, high-definition television, and security systems. Re-
constructed facial images can improve the accuracy of facial recognition systems and
offer a better visual experience.
However, the computational time and power required for real-time super-resolution
are often insufficient for facial images to reproduce.
The main contribution lies in improving existing techniques and designing a sys-
tem that can obtain high-resolution facial images with increased accuracy and fidelity.
Overall, the results of our study have shown a strong improvement in terms of visual
quality, edge sharpness, and preservation of facial details. We were able to surpass
existing methods by designing a specific CNN for face image super-resolution.
The future research directions could therefore focus on improving these aspects and
exploring new network architectures.
In summary, this research has made a significant contribution to the development of
a super-resolution system based on CNN for facial images. The results obtained are
interesting and give hope for improvements in various applications requiring high-
resolution images. However, further research is needed to overcome the identified
limitations and continue exploring the possibilities of image super-resolution.
