Department of Computer Engineering

Blind Super-Resolution methods aims to enhance the resolution of multiple different low resolution image counterparts of a single high resolution image with unknown degradation processes. With the advancements of various techniques in computer vision, many of the methods have been applied in Blind SR area and research has been mostly dominant with CNN based models. In order to take advantage of success
of transformers in computer vision area, we integrate transformers and degradation representations for a Blind SR model. Furthermore, with the observation of lack of peak performance compared to Non-Blind SR models, we separate the Blind SR problem. This sub-par performance in ideal settings is due to the inherent complexity in blind settings. To address this issue, we present a deblurring and denoising approach
specifically for Blind SR which serves as a preprocessing method. Specifically our preprocessing method standardizes the low resolution images to a single degradation, which then can be enhanced with Non Blind SR methods to achieve Blind SR performance. In addition to this approach we also use the same baseline architecture and propose an end to end Blind SR approach. We evaluate both our approaches on Set5,
Set14, B100 and Urban100 datasets and compare the results with other state-of-the-art approaches using PSNR metric.