Super-resolution using a light inception layer in convolutional neural network

Qinyang Mou; Jun Guo

doi:10.1117/12.2302514

10 April 2018 Super-resolution using a light inception layer in convolutional neural network

Qinyang Mou, Jun Guo

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106154R (2018) https://doi.org/10.1117/12.2302514
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Recently, several models based on CNN architecture have achieved great result on Single Image Super-Resolution (SISR) problem. In this paper, we propose an image super-resolution method (SR) using a light inception layer in convolutional network (LICN). Due to the strong representation ability of our well-designed inception layer that can learn richer representation with less parameters, we can build our model with shallow architecture that can reduce the effect of vanishing gradients problem and save computational costs. Our model strike a balance between computational speed and the quality of the result. Compared with state-of-the-art result, we produce comparable or better results with faster computational speed.

Citation Download Citation

Qinyang Mou and Jun Guo "Super-resolution using a light inception layer in convolutional neural network", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106154R (10 April 2018); https://doi.org/10.1117/12.2302514

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KEYWORDS

Super resolution

Convolution

Convolutional neural networks

Lawrencium

Performance modeling

Image filtering

RGB color model

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