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Lightweight Image Super-Resolution With Mobile Share-Source Network
DOI:10.1109/ACCESS.2020.2983079.png)
摘要
En 中文
Within the development of the deep convolutional neural network, the great achievements had been made in the single-image super-resolution (SISR) task. However, the higher performance always comes with the deeper layers which also brings larger numbers of network operations and parameters that make it hard to implement in practice. In our work, a lightly super-resolution, named Mobile Share- Source Network (MSSN), is purposed to address these practical issues. In MSSN, a high-efficiency block, the mobile adaptive weighted residual unit, is designed to fulfill the need for the reduction in both parameters and the Mult-Adds while maintaining the performance with importing the deep separable convolution. Moreover, it brings into the Adaptive Weighted Share-Source Skip Connection, getting abundant information from the shallow layer which helps reconstruct better images. The experimental results show that our network has fewer numbers of parameters and operations than the state-of-the-art lightweight network while maintaining high reconstruction quality comparing with many state-of-the-art super-resolution methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM).
Keyword:
Super-resolution
depthwise separable convolution
adaptive weighted module
lightweight network
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Deep Differential Convolutional Network for Single Image Super-Resolution面向单幅图像超分辨率的深度差分卷积网络
IEEE ACCESS
IF3.6
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