综合资源展示 综合资源展示

最小化 最大化
«返回

Compressed multi-scale feature fusion network for single image super-resolution

  • 详细信息
标题: Compressed multi-scale feature fusion network for single image super-resolution
资源摘要: Publication date: May 2018
Source:Signal Processing, Volume 146

Author(s): Xinxia Fan, Yanhua Yang, Cheng Deng, Jie Xu, Xinbo Gao

Recently, deep neural networks have made significant breakthroughs in the image super-resolution (SR) field. Most deep learning-based image SR methods learn an end-to-end network to discover the mapping relationship between low-resolution (LR) and high-resolution (HR) images in order to produce visually satisfactory images. However, these methods only extract a single scale feature to learn the mapping relationship, which will miss some critical information that is required for reconstruction. In this paper, we propose a compressed multi-scale feature fusion (MSFF) network for single image SR. Several MSFF modules are used in the network to extract image features at different scales, which enables us to capture more complete structure and context information of the image for better SR quality. Furthermore, to solve the problems of training difficulty and computational expense consumption caused by the use of the multi-scale structure, structure sparse regularization is designed to learn a MSFF network with a sparse structure and obtain a compressed network, which greatly reduces the network parameters and accelerates the speed whilst sustaining the reconstruction quality. Extensive experiments on a variety of images show that the proposed method can achieve more desirable performance in terms of visual quality than several state-of-the-art methods.





资源原始URL http://rss.sciencedirect.com/action/redirectFile?&zone=main¤tActivity=feed&usageType=outward&url=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%3F_ob%3DGatewayURL%26_origin%3DIRSSSEARCH%26_method%3DcitationSearch%26_piikey%3DS0165168417304309%26_version%3D1%26md5%3D53107de6abb402845b376a4a5c4d562e
资源来源机构: Elsevier
资源来源机构URL: http://rss.sciencedirect.com/getMessage?registrationId=JDGJJEGKQFGSKEGNLDHNJKHNJHIJLHKQSEIOJMJPSO
来源机构所属国家: 其他
来源机构性质:
您还没有登录。 请先登录再使用本系统。
您还没有登录。 请先登录再使用本系统。