Adaptive residual neural network for image super-resolution

Weiwei Li; Xinlong Li; Zhenbing Liu

doi:10.1117/12.2539329

14 February 2020 Adaptive residual neural network for image super-resolution

Weiwei Li, Xinlong Li, Zhenbing Liu

Proceedings Volume 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging; 1143107 (2020) https://doi.org/10.1117/12.2539329
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single image super resolution (SISR) field. However, most of existing CNN-based SR models require high computing power, which is not conducive to daily use. In addition, these algorithms need to use a large number of CNN to obtain global features. Therefore, this paper proposes an image super-resolution framework based on adaptive residual neural network, using the adaptive framework to switch between global and local reasoning for internal features in a flexible way, it can extract a large number of global features without neglecting key information, which is conducive to the comprehensiveness of residual images. After the adaptive block, SENet is added to conduct channel modeling for the extracted features, and the importance of each feature channel is automatically acquired by learning method. Then, according to this importance, useful features are promoted and those that are not useful for the current task are suppressed. In this way, with more nonlinearity, the complex correlation between channels can be better fitted, and the number of parameters and computation can be reduced, which can improve the performance of super resolution to a certain extent.

Citation Download Citation

Weiwei Li, Xinlong Li, and Zhenbing Liu "Adaptive residual neural network for image super-resolution", Proc. SPIE 11431, MIPPR 2019: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, 1143107 (14 February 2020); https://doi.org/10.1117/12.2539329

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KEYWORDS

Image quality

Feature extraction

Super resolution

Magnetic resonance imaging

Neural networks

Lawrencium

Convolution

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