Super-resolution Networks in Machine Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 2902
Special Issue Editor
Special Issue Information
Dear Colleagues,
Super resolution (SR) aims to increase the size of a given input low-resolution (LR) image by recovering high-frequency details. SR has gathered a lot of interest as high resolution displays are becoming increasingly common in many smartphones, TVs, and monitors, and thus have become an active research topic. In particular, with the aid of recent advances in deep learning, many state-of-the-art SR methods have produced promising results, and we are interested in articles that explore these learning-based SR approaches. Potential topics include, but are not limited to, the following:
- Image/video upsampling and super-resolution algorithms;
- Real-time image super resolution;
- Large-factor image super resolution;
- Real-image super resolution;
- Studies and applications of the above.
Dr. Tae Hyun Kim
Guest Editor
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Keywords
- computation imaging
- super-resolution
- enhancement
- upsampling
- machine learning
- deep learning
- CNN
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