Image Super-Resolution

11685 - Introduction to Deep Learning

Image x4 upscaling using Autoencoder, SRGAN and ESRGAN.

Authors:

• Yu Zhang
• Alejandro Alvarez

Description

The aim of this project is to implement x4 upscaling on low-resolution images from the dataset DIV2K using three different models:

• Convolutional Autoencoder
• Super-Resolution Generative Adversarial Network (SRGAN)
• Enhanced Super-Resolution Generative Adversarial Network (ESRGAN)

Performance

The metrics used for performance evaluation were Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).

References

• Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).

• Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., ... & Change Loy, C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops (pp. 0-0).