Enhanced Generative Learning Methods for Real-World Super-Resolution Problems in Smartphone Images

Type: BA thesis

Status: running

Date: June 21, 2021 - November 21, 2021

Supervisors: Prathmesh Madhu, Ronak Kosti, Andreas Maier

The goal of this bachelor thesis is to extend the work of Lugmayr et al. [1] in order to improve the generative network by using a learned image down sampler motivated from CAR network [2] instead of bicubic down sampling. The aim is to achieve a better image quality or a more robust SR network for images of Real-World data distribution.

[1] Lugmayr, Andreas, Martin Danelljan, and Radu Timofte. “Unsupervised learning for real-world super-resolution.” 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019.

[2] Sun, Wanjie, and Zhenzhong Chen. “Learned image downscaling for upscaling using content adaptive resampler.” IEEE Transactions on Image Processing 29 (2020): 4027-4040.