Edge guided reconstruction for compressive imaging. We propose EdgeCS -- an edge guided compressive sensing reconstruction approach -- to recover images of higher quality from fewer measurements than the current methods. Edges are important image features that are used in various ways in image recovery, analysis, and understanding. In compressive sensing, the sparsity of image edges has been successfully utilized to recover images. However, edge detectors have not been used on compressive sensing measurements to improve the edge recovery and subsequently the image recovery. This motivates us to propose EdgeCS, which alternatively performs edge detection and image reconstruction in a mutually beneficial way. par The edge detector of EdgeCS is designed to faithfully return partial edges from intermediate image reconstructions even though these reconstructions may still have noise and artifacts. For complex-valued images, it incorporates joint sparsity between the real and imaginary components. EdgeCS has been implemented with both isotropic and anisotropic discretizations of total variation and tested on incomplete $k$-space (spectral Fourier) samples. It applies to other types of measurements as well. Experimental results on large-scale real/complex-valued phantom and magnetic resonance (MR) images show that EdgeCS is fast and returns high-quality images. For example, it exactly recovers the $256 imes 256$ Shepp-Logan phantom from merely 7 radial lines ($3.03%$ $k$-space), which is impossible for most existing algorithms. It is able to accurately reconstruct a $512 imes 512$ MR image with 0.05 white noise from $20.87%$ radial samples. On complex-valued MR images, it obtains recoveries with faithful phases, which are important in many medical applications. Each of these tests took around 30 seconds on a standard PC. Finally, the algorithm is GPU friendly.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Qin, Jing; Guo, Weihong: Two-stage geometric information guided image reconstruction (2021)
  2. Yan, Mengyuan; Duan, Yuping: Nonlocal elastica model for sparse reconstruction (2020)
  3. Churchill, Victor; Gelb, Anne: Detecting edges from non-uniform Fourier data via sparse Bayesian learning (2019)
  4. Gong, Changcheng; Zeng, Li; Wang, Chengxiang: Image reconstruction model for limited-angle CT based on prior image induced relative total variation (2019)
  5. Rahimi, Yaghoub; Wang, Chao; Dong, Hongbo; Lou, Yifei: A scale-invariant approach for sparse signal recovery (2019)
  6. Tong, Shanshan; Han, Bo; Tang, Jinping: Edge-guided TV(^p) regularization for diffuse optical tomography based on radiative transport equation (2018)
  7. Guo, Weihong; Song, Guohui; Zhang, Yue: PCM-TV-TFV: a novel two-stage framework for image reconstruction from Fourier data (2017)
  8. Ma, Tian-Hui; Lou, Yifei; Huang, Ting-Zhu: Truncated (l_1-2) models for sparse recovery and rank minimization (2017)
  9. He, Chuan; Hu, Changhua; Li, Xuelong; Zhang, Wei: A parallel primal-dual splitting method for image restoration (2016)
  10. Schaeffer, Hayden; Yang, Yi; Osher, Stanley: Space-time regularization for video decompression (2015)
  11. Xie, Wei-Si; Yang, Yu-Fei; Zhou, Bo: An ADMM algorithm for second-order TV-based MR image reconstruction (2014)
  12. Guo, Weihong; Yin, Wotao: Edge guided reconstruction for compressive imaging (2012)