Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.
In the proposed framework, Deep Neural Networks (DNNs) are used to learn the characteristics of the PAs, while, correspondent Digital Pre-Distortions (DPDs) are also learned to compensate for the nonlinear and memory effects of PAs.
We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones.
2 code implementations • 27 Apr 2020 • Cheng-Ming Chiang, Yu Tseng, Yu-Syuan Xu, Hsien-Kai Kuo, Yi-Min Tsai, Guan-Yu Chen, Koan-Sin Tan, Wei-Ting Wang, Yu-Chieh Lin, Shou-Yao Roy Tseng, Wei-Shiang Lin, Chia-Lin Yu, BY Shen, Kloze Kao, Chia-Ming Cheng, Hung-Jen Chen
To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices.
In SISR with non-blind setting, our Unified Dynamic Convolutional Network for Variational Degradations (UDVD) is evaluated on both synthetic and real images with an extensive set of variations.
Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field.