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arXiv · 2008.00239

Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution

Ruicheng Feng·Weipeng Guan·Yu Qiao·Chao Dong
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Abstract

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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Ruicheng Feng, Weipeng Guan, Yu Qiao, Chao Dong. 2020-08-01. Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution. https://arxiv.org/abs/2008.00239

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