• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >Image compressive sensing via hybrid regularization combining centralized group sparse representation and deep denoiser prior
【24h】

Image compressive sensing via hybrid regularization combining centralized group sparse representation and deep denoiser prior

机译：Image compressive sensing via hybrid regularization combining centralized group sparse representation and deep denoiser prior

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

To effectively solve the ill-posed image compressive sensing (CS) reconstruction problem, it is essential to properly exploit image prior knowledge. In this paper, we propose an efficient hybrid regularization approach for image CS reconstruction, which can simultaneously exploit both internal and external image priors in a unified framework. Specifically, a novel centralized group sparse representation (CGSR) model is designed to more effectively exploit internal image sparsity prior by suppressing the group sparse coding noise (GSCN), i.e., the difference between the group sparse coding coefficients of the observed image and those of the original image. Meanwhile, by taking advantage of the plug-and-play (PnP) image restoration framework, a state-of-the-art deep image denoiser is plugged into the optimization model of image CS reconstruction to implicitly exploit external deep denoiser prior. To make our hybrid internal and external image priors regularized image CS method (named as CGSR-D-CS) tractable and robust, an efficient algorithm based on the split Bregman iteration is developed to solve the optimization problem of CGSR-D-CS. Experimental results demonstrate that our CGSR-D-CS method outperforms some state-of-the-art image CS reconstruction methods (either model-based or deep learning-based methods) in terms of both objective quality and visual perception.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号