RETRACTED: A compensating total variation image denoising model combining L 1 and L 2 norm

Buades A Coll B Morel JM. A non-local algorithm for image denoising. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, San Diego, CA, USA, 20–25 June 2005, pp.60–65. Piscataway, NJ: IEEE.

Sudeep PV Palanisamy P Rajan J , et al. Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomed Signal Process Control 2016; 28: 1–8.

Prabusankarlal KM Manavalan R Sivaranjani R. An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images. Appl Comput Inform 2018; 14: 48–54.

Draskovic G Pascal F Tupin F. M-NL: robust NL-means approach for PolSAR images denoising. IEEE Geosci Remote Sensing Lett 2019; 16: 1–5.

Dabov K Foi A Katkovnik V , et al. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 2007; 16: 2080–2095.

Hasan M El-Sakka MR. Improved BM3D image denoising using SSIM-optimized Wiener filter. EURASIP J Image Video Process. Epub ahead of print 17 April 2018. DOI: 10.1186/s13640-018-0264-z.

Tibbs AB Daly IM Roberts NW , et al. Denoising imaging polarimetry by adapted BM3D method. J Opt Soc Am A Opt Image Sci Vis 2018; 35: 690–701.

Yang JY Xue Z Yue HJ , et al. IBM3D: integer BM3D for efficient image denoising. Circuit Syst Signal Process 2019; 38: 750–763.

Tebini S Mbarki Z Seddik H , et al. Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function. Digital Signal Process 2016; 48: 201–215.

Abdallah MB Malek J Azar AT , et al. Adaptive noise-reducing anisotropic diffusion filter. Neural Comput Appl 2016; 27: 1273–1300.

Zhou ZH Lam EY Lee C. Nonlocal means filtering based speckle removal utilizing the maximum a posteriori estimation and the total variation image prior. IEEE Access 2019; 7: 99231-99243.

Huber R Haberfehlner G Holler M , et al. Total generalized variation regularization for multi-modal electron tomography. Nanoscale 2019; 11: 5617–5632.

Rafsanjani HK Sedaaghi MH Saryazdi S. An adaptive diffusion coefficient selection for image denoising. Digital Signal Process 2017; 64: 71–82.

Rudin LI Osher S Fatemi E. Nonlinear total variation based noise removal algorithms. Phys D Nonlinear Phenom 1992; 60: 259–268.

Ahlad K Ahmad MO Swamy MNS. Tchebichef and adaptive steerable based total variation model for image denoising. IEEE Trans Image Process 2019; 28: 2921–2935.

Chen MJ Zhang H Han Q , et al. A convex nonlocal total variation regularization algorithm for multiplicative noise removal. J Image Video Proc 2019, (1): 1–2.

Zhang HY Peng QC. Adaptive image denoising model based on total variation. Opto-Electron Eng 2006; 33: 50–53.

Abdelgader S Guo ZC Zhou ZY , et al. An image denoising model based on a fourth-order nonlinear partial differential equation. Comput Math Appl 2018; 76: 1056–1074.

Gilboa G Osher S. Nonlocal operators with applications to image processing. Multiscale Model Simul 2008; 7: 1005–1028.

Nie XL Qiao H Zhang B , et al. A nonlocal TV-based variational method for PolSAR data speckle reduction. IEEE Trans Image Process 2016; 25: 2620–2634.

Shen Y Liu Q Lou SQ , et al. Wavelet-based total variation and nonlocal similarity model for image denoising. IEEE Signal Process Lett 2017; 24: 877–881.

Zhang GM Sun XX Liu JX. Fractional total variation denoising model based on adaptive projection algorithm. Pattern Recogn Artif Intell 2016; 29: 1009–1018.

Ma QT Dong FF Kong DX. A fractional differential fidelity-based PDE model for image denoising. Mach Vis Appl 2017; 28: 635–647.

Zhu YG Zhang GM. Fractional total variation denoising algorithm based on an adaptive residual image. J Image Graph 2017; 22: 1677–1689.

He K Zheng XQ Ju SG , et al. Image denoising on improved total variation. J Univ Electron Sci Technol China 2016; 45: 463–468.

Duan PH Kang XD Li ST , et al. Noise-robust hyperspectral image classification via multi-scale total variation. IEEE J Sel Top Appl Earth Obser Remote Sens 2019; 12: 1–15.

Gao TL Wang XF Liu Q , et al. A fixed-point algorithm for second-order total variation models in image denoising. Comp Appl Math 2019; 38: 8.

Bai JQ Chen Q. Algorithm for infrared image noise filtering based on anisotropic diffusion. Acta Opt Sin 2008; 28: 866–869.

Sun W Wang ZJ. Fuzzy edge detection based on standard deviation gradient. Infrared Laser Eng 2005; 34: 454–458.

Zhao WD. Variation method and partial differential equation-based image enhancement and fusion. In: Changchun Institute of optics, fine mechanics and physics, Changchun, China, 2016: 16–19. Beijing, China: Chinese Academy of Sciences.

Wang Z Bovik AC Sheikh HR , et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004; 13: 600–612.