Evaluation of performance of VDSR super resolution on real and synthetic images

D. Vint, G. Di Caterina, J. J. Soraghan, R. A. Lamb, D. Humphreys

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)
241 Downloads (Pure)


This paper presents an evaluation of the suitability of the Very Deep Super Resolution (VDSR) architecture, to increase the spatial resolution of lower quality images. For this aim, two sets of tests are performed. The former being on real life images to determine the networks ability to improve low resolution images. The second test is performed on images of a resolution chart, and therefore synthetic. This is to analyse the frequency response of the network. For each test, three metrics are used to assess image quality. These are the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Modulation Transfer Function (MTF). Experimental results show that the VDSR network is able to increase the quality of the images within the first test in all three metrics, therefore showing that the network is suitable for super resolution. The second test provides more information on the limitations of the network when given a high contrast image, and the resulting ringing effects it can create. Therefore results in PSNR/SSIM values are not improved over the low resolution images, however they have a higher MTF curve as well as more visually sharp images.

Original languageEnglish
Number of pages5
Publication statusPublished - 9 May 2019
EventSensor Signal Processing for Defence 2019 - Brighton, United Kingdom
Duration: 9 May 201910 May 2019


ConferenceSensor Signal Processing for Defence 2019
Abbreviated titleSSPD'19
Country/TerritoryUnited Kingdom


  • diffraction
  • deep learning
  • VDSR
  • SSIM
  • MTF


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