Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
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Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a comprehensive investigation on variants of multi-scale convolution in image super resolution. In this work, we present a unified formulation over widely-used multi-scale structures. With this framework, we systematically explore the two factors of multi-scale convolution – feature propagation and cross-scale communication. Based on the investigation, we propose a generic and efficient multi-scale convolution unit – Multi-Scale cross-Scale Share-weights convolution (MS^3-Conv). Extensive experiments demonstrate that the proposed MS^3-Conv can achieve better SR performance than the standard convolution with less parameters and computational cost. Beyond quantitative analysis, we comprehensively study the visual quality, which shows that MS^3-Conv behave better to recover high-frequency details.
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