Blind Image Super Resolution Using Deep Unsupervised Learning

Abstract

The goal of single image super resolution (SISR) is to recover a high-resolution (HR) image from a low-resolution (LR) image. Deep learning based methods have recently made a remarkable performance gain in terms of both the effectiveness and efficiency for SISR. Most existing methods have to be trained based on large-scale synthetic paired data in a fully supervised manner. With the available HR natural images, the corresponding LR images are usually synthesized with a simple fixed degradation operation, such as bicubic down-sampling. Then, the learned deep models with these training data usually face difficulty to be generalized to real scenarios with unknown and complicated degradation operations. This study exploits a novel blind image super-resolution framework using a deep unsupervised learning network. The proposed method can simultaneously predict the underlying HR image and its specific degradation operation from the observed LR image only without any prior knowledge. The experimental results on three benchmark datasets validate that our proposed method achieves a promising performance under the unknown degradation models.

Blind Super-Resolution Network with Dual-Channel Attention for Images Captured by Sub-Millimeter-Diameter Fiberscope

“…The pixels of the image decrease as the number of fiber bundles is reduced, and there is also reticulated noise in the image. Therefore, the degradation process of a sub-millimeter-diameter fiberscope image can be expressed by the following equation [24]:…”