Search Results for author:
Found 11 papers, 5 papers with code
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
Towards this goal, we propose AdaIR, a novel framework that enables low storage cost and efficient training without sacrificing performance.
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations.
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.
Learning to Compensate: A Deep Neural Network Framework for 5G Power Amplifier Compensation
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.
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos.
Network Space Search for Pareto-Efficient Spaces
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.
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices.
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
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.
