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- W4312910893 abstract "A deep learning technique for the super-resolution of a single image. With our approach, spatial dependencies are captured and end-to-end mapping between the low/high-resolution images is learned. A deep convolutional neural network (CNN) is used which accepts the low-resolution images as input and produces the high-resolution ones used to represent the mapping. This model demonstrates a lightweight construction, high restoration quality, and quick performance for practical online usage. This paper investigates multiple network architectures and parameter settings to accomplish trade-offs between performance and speed. Furthermore, our model is built to handle three color channels at the same time and demonstrate improved overall reconstruction quality." @default.
- W4312910893 created "2023-01-05" @default.
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- W4312910893 date "2022-10-16" @default.
- W4312910893 modified "2023-10-05" @default.
- W4312910893 title "Image Super-Resolution Using Convolutional Neural Network" @default.
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- W4312910893 doi "https://doi.org/10.1109/mysurucon55714.2022.9972459" @default.
- W4312910893 hasPublicationYear "2022" @default.
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