Paper Info
Title
FPGA implementation of a real-time super-resolution system using a convolutional neural network
Abstract
Super-resolution technologies are used to fill the gap between high-resolution displays and lower-resolution contents. There are various algorithms to interpolate information, one of which is using a convolutional neural network (CNN). This paper shows FPGA implementation and performance evaluation of a CNN-based super-resolution system, which can process moving images in real time. We apply horizontal and/or vertical flips to network input images instead of commonly used pre-enlargement techniques. This method prevents information loss and enables the network to utilize the best of its input image size. Our system can perform super-resolution from 960×540 pixels to 1920×1080 pixels at not less than 48fps with a latency of less than 1 ms. Even though the network scale and the size of filters are limited due to resource restriction of the FPGA, the system generates clear super-resolution images with smooth edges. The evaluation results also reveal that the proposed system achieves superior quality in terms of the structural similarity (SSIM) index, compared to other systems using pre-enlargement.
Year
DOI
Venue
2016
10.1109/FPT.2016.7929545
2016 International Conference on Field-Programmable Technology (FPT)
Keywords
Field
DocType
FPGA implementation,real-time superresolution system,convolutional neural network,CNN,network input images,resource restriction
Computer vision,Convolutional neural network,Latency (engineering),Computer science,Interpolation,Field-programmable gate array,Real-time computing,Pixel,Artificial intelligence,Image restoration,Artificial neural network,Image resolution
Conference
ISBN
Citations 
PageRank 
978-1-5090-5603-3
1
0.63
References 
Authors
0
3
Name
Order
Citations
PageRank
Taito Manabe133.15
Yuichiro Shibata215737.99
Kiyoshi Oguri314633.63