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Found 5 papers, 2 papers with code
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.
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.
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.
