FTVd: A Fast Algorithm for Total Variation based Deconvolution. FTVd refers to Fast Total Variation (TV) deconvolution, and is a TV based deconvolution / denoising package. The latest package includes fast solvers for the TV/L2 and TV/L1 models, which are compatible with both grayscale and color images. FTVd can be easily modified to work with three and higher dimensional image/data.

References in zbMATH (referenced in 71 articles )

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  1. Adam, Tarmizi; Paramesran, Raveendran; Ratnavelu, Kuru: A combined higher order non-convex total variation with overlapping group sparsity for Poisson noise removal (2022)
  2. Chen, Pengwen: Local saddles of relaxed averaged alternating reflections algorithms on phase retrieval (2022)
  3. Chen, Yang; Wu, Chunlin: Data-driven tight frame construction for impulsive noise removal (2022)
  4. Huang, Yu-Mei; Yan, Hui-Yin: Weighted nuclear norm minimization-based regularization method for image restoration (2021)
  5. Liu, Jingjing; Ma, Ruijie; Zeng, Xiaoyang; Liu, Wanquan; Wang, Mingyu; Chen, Hui: An efficient non-convex total variation approach for image deblurring and denoising (2021)
  6. Luo, Zhijun; Zhu, Zhibin; Zhang, Benxin: A LogTVSCAD nonconvex regularization model for image deblurring in the presence of impulse noise (2021)
  7. Xue, Feng; Ai, Xia; Liu, Jiaqi: On the convergence of recursive SURE for total variation minimization (2021)
  8. Zhong, Qiuxiang; Yin, Ke; Duan, Yuping: Image reconstruction by minimizing curvatures on image surface (2021)
  9. Hong, Mingyi; Chang, Tsung-Hui; Wang, Xiangfeng; Razaviyayn, Meisam; Ma, Shiqian; Luo, Zhi-Quan: A block successive upper-bound minimization method of multipliers for linearly constrained convex optimization (2020)
  10. Huai, Kaizhan; Ni, Mingfang; Wang, Lei; Yu, Zhanke; Yang, Jing: A linearized Peaceman-Rachford splitting method for structured convex optimization with application to stable principal component pursuit (2020)
  11. Hu, Leyu; Zhang, Wenxing; Cai, Xingju; Han, Deren: A parallel operator splitting algorithm for solving constrained total-variation retinex (2020)
  12. Liu, Xiaoman; Liu, Jijun: Image restoration from noisy incomplete frequency data by alternative iteration scheme (2020)
  13. Shen, Yuan; Liu, Xin: An alternating minimization method for matrix completion problems (2020)
  14. Kang, Myeongmin; Kang, Myungjoo; Jung, Miyoun: Sparse representation based image deblurring model under random-valued impulse noise (2019)
  15. Lee, Chang-Ock; Nam, Changmin; Park, Jongho: Domain decomposition methods using dual conversion for the total variation minimization with (L^1) fidelity term (2019)
  16. Lu, Jian; Qiao, Ke; Li, Xiaorui; Lu, Zhaosong; Zou, Yuru: (\ell_0)-minimization methods for image restoration problems based on wavelet frames (2019)
  17. Lu, Jian; Tian, Jiapeng; Shen, Lixin; Jiang, Qingtang; Zeng, Xueying; Zou, Yuru: Rician noise removal via a learned dictionary (2019)
  18. Shen, Yuan; Ji, Lei: Partial convolution for total variation deblurring and denoising by new linearized alternating direction method of multipliers with extension step (2019)
  19. Zhang, Xiongjun; Ng, Michael K.: A fast algorithm for solving linear inverse problems with uniform noise removal (2019)
  20. Cui, Zhuo-Xu; Fan, Qibin: A “nonconvex+nonconvex” approach for image restoration with impulse noise removal (2018)

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