2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-4.1
Paper Title DEEP BLIND UN-SUPERVISED LEARNING NETWORK FOR SINGLE IMAGE SUPER RESOLUTION
Authors Kazuhiro Yamawaki, Xian-Hua Han, Yamaguchi University, Japan
SessionTEC-4: Super-resolution
LocationArea G
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Image and Video Processing: Interpolation, super-resolution, and mosaicing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, image super-resolution (SR), which predicts high-resolution (HR) images from low-resolution (LR) images, has shown increasing performance improvements with the progress of deep learning. Most of the current methods have struggled to design more complicated and deeper network architectures and aimed to learn a good LR-to-HR mapping with the previously prepared training sample pairs under a fixed degradation model (Blurring and down-sampling operations) such as bicubic dawn-sampling. However, these methods cannot be generalized to real scenarios with unknown complex degradation models. In this study, we propose a blind unsupervised learning network for automatically estimating the unknown degradation operations in a single SR problem. Motivated by the considerable possessed image priors in the network architectures, we construct a generative network for simultaneously learning the inherent priors of the latent LR low-resolution observation only. Experimental results on two benchmark datasets demonstrate that the proposed method shows promising performance under unknown degradation models.