Super Resolution

Super resolution aims to recover a plausible high resolution version of a low resolution image. The main techniques that were used to solve this problem were interpolation, enhancing linear methods and patch-based methods. Nowadays, state of the art in super resolution is Deep Convolution Neural Networks. These networks exploit low and high level image features to reconstruct perceptually realistic photos and videos.

Some examples of Image SuperResolution

Some examples of Video SuperResolution

The research behind

The network architecture basis for SuperResolution


  • Input: Low resolution images (RGB)
  • Output: Super resolution images (RGB)
  • 3-layer network with one subpixel convolution network
Visual Computing Lab

The focus of the Visual Computing Laboratory is to develop new algorithms and architectures for applications in the areas of 3D processing, image/video processing, computer vision, pattern recognition, bioinformatics and medical imaging.

Contact Information

Dr. Petros Daras, Principal Researcher Grade Α
1st km Thermi – Panorama, 57001, Thessaloniki, Greece
P.O.Box: 60361
Tel.: +30 2310 464160 (ext. 156)
Fax: +30 2310 464164
Email: daras@iti.gr