• Machine Learning & Data Mining •

Method for super-resolution using parallel interlaced sampling

An ZHU1(),Chu XU2

1. 1. Software Institute, Nanjing University, Nanjing 210000, Jiangsu, China
2. Information Construction Management Service Center, Nanjing University Information Construction Management Service Center, Nanjing 210000, Jiangsu, China
• Received:2019-06-20 Online:2020-04-20 Published:2020-04-16

Abstract:

Various Internet-based images and artificial intelligence applications were more sensitive to the quality of image data. The image quality had been seriously affected due to the limitations of previous acquisition equipment and transmission methods. In order to compensate for the loss of image data quality and enhance the image effect, a parallel interlaced up and down sampling network (PSUDN) was proposed as a better solution to this problem, which using parallel high resolution feature (HR Feature) and low resolution feature (LR Feature) interleaving sample to generated advanced feature maps, and improved the quality of the output high-resolution pictures by building parallel high resolution feature modules and low resolution feature modules. The model constructed by parallel upsampling and downsampling could reconstruct 8 times high resolution pictures and achieved better results.

CLC Number:

• TP319
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