Abstract
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 language | English |
|---|---|
| Number of pages | 5 |
| Publication status | Published - 9 May 2019 |
| Event | Sensor Signal Processing for Defence 2019 - Brighton, United Kingdom Duration: 9 May 2019 → 10 May 2019 |
Conference
| Conference | Sensor Signal Processing for Defence 2019 |
|---|---|
| Abbreviated title | SSPD'19 |
| Country | United Kingdom |
| City | Brighton |
| Period | 9/05/19 → 10/05/19 |
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Keywords
- diffraction
- deep learning
- VDSR
- SSIM
- MTF
Cite this
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Evaluation of performance of VDSR super resolution on real and synthetic images. / Vint, D.; Di Caterina, G.; Soraghan, J. J.; Lamb, R. A.; Humphreys, D.
2019. Paper presented at Sensor Signal Processing for Defence 2019, Brighton, United Kingdom.Research output: Contribution to conference › Paper
TY - CONF
T1 - Evaluation of performance of VDSR super resolution on real and synthetic images
AU - Vint, D.
AU - Di Caterina, G.
AU - Soraghan, J. J.
AU - Lamb, R. A.
AU - Humphreys, D.
PY - 2019/5/9
Y1 - 2019/5/9
N2 - 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.
AB - 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.
KW - diffraction
KW - deep learning
KW - VDSR
KW - SSIM
KW - MTF
UR - https://sspd.eng.ed.ac.uk/
M3 - Paper
ER -