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Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network
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DOI：10.1109/JSTSP.2020.3045282.png)
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Deep neural networks have achieved remarkable success in single image super-resolution (SISR). However, in most cases, image SR with different scale factors is considered as different tasks and solved by training specific models. It makes the image SR applications inefficient and tedious. Hence, to tackle these problems, we propose a lightweight and fast network (MSWSR) to implement multi-scale SR simultaneously by learning multi-level wavelet coefficients of the target image. The proposed network is composed of one CNN part and one RNN part. The CNN part is used for predicting the highest-level low-frequency wavelet coefficients, while the RNN part is used for predicting the rest frequency bands of wavelet coefficients. Moreover, the RNN part is extendable to more scales. For further lightweight, a non-square (side window) convolution kernel is proposed to reduce the network parameters. Experiments on commonly-used datasets demonstrate that the proposed method achieves favorable reconstruction performance with a fast speed and lightweight network. The code is available at https://github.com/FVL2020/MSWSR.
Keyword:
Wavelet transforms
Convolution
Image reconstruction
Task analysis
Kernel
Feature extraction
Recurrent neural networks
Lightweight and fast network
multi-scale super-resolution
recurrent learning
side window convolution
wavelet prediction
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