American Institute of Mathematical Sciences

Advanced
June  2019, 13(3): 461-478. doi: 10.3934/ipi.2019023

A variational gamma correction model for image contrast enhancement

Wei Wang 1, , Na Sun 1, and  Michael K. Ng 2,,

1. 

School of Mathematical Sciences, Tongji University, Shanghai, China
2. 

Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

* Corresponding author: Michael K. Ng

Received  February 2018 Revised  November 2018 Published  March 2019

Fund Project: W. Wang is supported by Natural Science Foundation of Shanghai and Fundamental Research Funds for the Central Universities of China (22120180255, 22120180067), Michael K. Ng is supported in part by HKRGC GRF 12306616 and 12200317

Image contrast enhancement plays an important role in computer vision and pattern recognition by improving image quality. The main aim of this paper is to propose and develop a variational model for contrast enhancement of color images based on local gamma correction. The proposed variational model contains an energy functional to determine a local gamma function such that the gamma values can be set according to the local information of the input image. A spatial regularization of the gamma function is incorporated into the functional so that the contrast in an image can be modified by using the information of each pixel and its neighboring pixels. Another regularization term is also employed to preserve the ordering of pixel values. Theoretically, the existence and uniqueness of the minimizer of the proposed model are established. A fast algorithm can be developed to solve the resulting minimization model. Experimental results on benchmark images are presented to show that the performance of the proposed model are better than that of the other testing methods.

Keywords: Contrast enhancement, variational method, gamma correction, algorithm, minimization.
Mathematics Subject Classification: Primary: 65J22, 65K10, 68U10; Secondary: 65K15.
Citation: Wei Wang, Na Sun, Michael K. Ng. A variational gamma correction model for image contrast enhancement. Inverse Problems & Imaging, 2019, 13 (3) : 461-478. doi: 10.3934/ipi.2019023
References:
[1]

T. Arici, S. Dikbas and Y. Altunbasak, A histogram modification framework and its application for image contrast enhancement, IEEE Transactions on Image Processing, 18 (2009), 1921-1935.  doi: 10.1109/TIP.2009.2021548.  Google Scholar

[2]

A. Beghdadi and A. L. Negrate, Contrast enhancement technique based on local detection of edges, Comput. Vis, Graph., Image Process., 46 (1989), 162-174.   Google Scholar

[3]

R. Chan, M. Nikolova and Y. Wen, A variational approach for exact histogram specification, Scale Space and Variational Methods in Computer Vision, (2012), 86–97. Google Scholar

[4]

S. D. Chen and A. R. Ramli, Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309.   Google Scholar

[5]

H.-D. Cheng and H. J. Xu, A novel fuzzy logic approach to contrast enhancement, Pattern Recognition, 33 (2000), 809-819.   Google Scholar

[6]

Y.-S. Chiu, F.-C. Cheng and S.-C. Huang, Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution, IEEE International Conference on Systems, Man, and Cybernetics, 22 (2013), 1032-1041.  doi: 10.1109/TIP.2012.2226047.  Google Scholar

[7]

S. Deivalakshmi, A. Saha and R. Pandeeswari, Raised Cosine Adaptive Gamma Correction for Efficient Image and Video Contrast Enhancement, TENCON 2017-2017 IEEE Region 10 Conference, Nov. 2017. Google Scholar

[8]

J. Eckstein and D. Bertsekas, On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators, Mathematical Programming, 55 (1992), 293-318.  doi: 10.1007/BF01581204.  Google Scholar

[9]

L. C. Evans, Partial Differential Equations, AMS, Providence, RI, 1998.  Google Scholar

[10]

M. FarshbafDoustar and H. Hassanpour, A locally-adaptive approach for image gamma correction, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010, 73–76. Google Scholar

[11]

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd Edition, Prentice Hall, 2002. Google Scholar

[12]

S.-C. Huang, F.-C. Cheng and Y.-S. Chiu, Efficient contrast enhancement using adaptive gamma correction with weighting distribution, IEEE Transactions on Image Processing, 22 (2013), 1032-1041.  doi: 10.1109/TIP.2012.2226047.  Google Scholar

[13]

A. Laine, J. Fan and W. Yang, Wavelets for contrast enhancement of digital mammography, IEEE Engineering in Medicine and Biology Magazine, 14 (1995), 536-550.   Google Scholar

[14]

Y. Li, X. Liu and Y. Liu, Adaptive local gamma correction based on mean value adjustment, 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015, 1858–1863. Google Scholar

[15]

S. C. Matz and R. J. P. de Figueiredo, A nonlinear image contrast sharpening approach based on munsell's scale, IEEE Transactions on Image Processing, 15 (2016), 900-909.   Google Scholar

[16]

M. Nikolova, A fast algorithm for exact histogram specification. simple extension to colour images, Scale Space and Variational Methods in Computer Vision, 2013,174–185. Google Scholar

[17]

M. Nikolova and G. Steidl, Fast hue and range preserving histogram specification: Theory and new algorithms for color image enhancement, IEEE Transactions on Image Processing, 23 (2014), 4087-4100.  doi: 10.1109/TIP.2014.2337755.  Google Scholar

[18]

M. Nikolova and G. Steidl, Fast ordering algorithm for exact histogram specification, IEEE Transactions on Image Processing, 23 (2014), 5274-5283.  doi: 10.1109/TIP.2014.2364119.  Google Scholar

[19]

M. Nikolova, Y. Wen and R. Chan, Exact histogram specification for digital images using a variational approach, Journal of Mathematical Imaging and Vision, 46 (2013), 309-325.  doi: 10.1007/s10851-012-0401-8.  Google Scholar

[20]

M. K. Ng and W. Wang, A total variation model for retinex, SIAM J. Imaging Sciences, 4 (2011), 345-365.  doi: 10.1137/100806588.  Google Scholar

[21]

F. Pierre, J. F. Aujol, A. Bugeau, G. Steidl and V. T. Ta, Variational contrast enhancement of gray-scale and RGB images, Journal of Mathematical Imaging and Vision, 57 (2017), 99-116.  doi: 10.1007/s10851-016-0670-8.  Google Scholar

[22]

F. Pierre, J. F. Aujol, A. Bugeau, G. Steidl and V. T. Ta, Hue-preserving perceptual contrast enhancement, Image Processing (ICIP), IEEE International Conference on, 2016, 4067–4071. Google Scholar

[23]

A. Polesel, G. Ramponi and V. Mathews, Image enhancement via adaptive unsharp masking, IEEE Trans. Image Process., 9 (2000), 505-510.   Google Scholar

[24]

E. Provenzi and V. Caselles, A wavelet perspective on variational perceptually-inspired color enhancement, International Journal of Computer Vision, 106 (2014), 153-171.  doi: 10.1007/s11263-013-0651-y.  Google Scholar

[25]

A. Rizzi, C. Gatta and D. Marini, A new algorithm for unsupervised global and local color correction, Pattern Recognition Letters, 24 (2003), 1663-1677.   Google Scholar

[26] J. C. Russ, The Image Processing Handbook, Fifth edition. CRC Press, Boca Raton, FL, 2007.   Google Scholar
[27]

C. E. Shannon, A mathematical theory of communication, Bell System Technical Jornal, 27 (1948), 379-423.  doi: 10.1002/j.1538-7305.1948.tb01338.x.  Google Scholar

[28]

R. Sherrier and G. Johnson, Regionally adaptive histogram equalization of the chest, IEEE Trans. Med. Imag., MI-6 (1987), 1-7.   Google Scholar

[29]

J. F. Shi and Y. Cai, A novel image enhancement method using local Gamma correction with three-level thresholding, Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, 2011,374–378. Google Scholar

[30]

Y. H. Shi, J. F. Yang and R. B. Wu, Reducing illumination based on nonlinear Gamma correction, Proceedings of the IEEE International Conference on Image Processing, (2007), 529–532. Google Scholar

[31]

J. Tang, X. Liu and Q. Sun, A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms, IEEE J.Sel. Topics Signal Process., 3 (2009), 74-80.   Google Scholar

[32]

Z. Wang, A. Bovik, H. Sheikh and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. on Image Processing, 13 (2004), 600–612. Google Scholar

[33]

W. Wang, C. Chen, M. K. Ng, An Image Pixel Based Variational Model for Histogram Equalization, Journal of Visual Communication and Image Representation, vol. 34, pp. 118-134, January 2016. Google Scholar

[34]

W. Wang and M. K. Ng, A variational histogram equalization method for image contrast enhancement, SIAM J. Imaging Sciences, 6 (2013), 1823-1849.  doi: 10.1137/130909196.  Google Scholar

[35]

G. Xu, J. Su and H. D. Pan, An image enhancement method based on Gamma correction, Proceedings of the 2nd International Symposium on Computational Intelligence and Design, 2009, 60–63. Google Scholar

show all references

References:
[1]

T. Arici, S. Dikbas and Y. Altunbasak, A histogram modification framework and its application for image contrast enhancement, IEEE Transactions on Image Processing, 18 (2009), 1921-1935.  doi: 10.1109/TIP.2009.2021548.  Google Scholar

[2]

A. Beghdadi and A. L. Negrate, Contrast enhancement technique based on local detection of edges, Comput. Vis, Graph., Image Process., 46 (1989), 162-174.   Google Scholar

[3]

R. Chan, M. Nikolova and Y. Wen, A variational approach for exact histogram specification, Scale Space and Variational Methods in Computer Vision, (2012), 86–97. Google Scholar

[4]

S. D. Chen and A. R. Ramli, Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309.   Google Scholar

[5]

H.-D. Cheng and H. J. Xu, A novel fuzzy logic approach to contrast enhancement, Pattern Recognition, 33 (2000), 809-819.   Google Scholar

[6]

Y.-S. Chiu, F.-C. Cheng and S.-C. Huang, Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution, IEEE International Conference on Systems, Man, and Cybernetics, 22 (2013), 1032-1041.  doi: 10.1109/TIP.2012.2226047.  Google Scholar

[7]

S. Deivalakshmi, A. Saha and R. Pandeeswari, Raised Cosine Adaptive Gamma Correction for Efficient Image and Video Contrast Enhancement, TENCON 2017-2017 IEEE Region 10 Conference, Nov. 2017. Google Scholar

[8]

J. Eckstein and D. Bertsekas, On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators, Mathematical Programming, 55 (1992), 293-318.  doi: 10.1007/BF01581204.  Google Scholar

[9]

L. C. Evans, Partial Differential Equations, AMS, Providence, RI, 1998.  Google Scholar

[10]

M. FarshbafDoustar and H. Hassanpour, A locally-adaptive approach for image gamma correction, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010, 73–76. Google Scholar

[11]

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd Edition, Prentice Hall, 2002. Google Scholar

[12]

S.-C. Huang, F.-C. Cheng and Y.-S. Chiu, Efficient contrast enhancement using adaptive gamma correction with weighting distribution, IEEE Transactions on Image Processing, 22 (2013), 1032-1041.  doi: 10.1109/TIP.2012.2226047.  Google Scholar

[13]

A. Laine, J. Fan and W. Yang, Wavelets for contrast enhancement of digital mammography, IEEE Engineering in Medicine and Biology Magazine, 14 (1995), 536-550.   Google Scholar

[14]

Y. Li, X. Liu and Y. Liu, Adaptive local gamma correction based on mean value adjustment, 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015, 1858–1863. Google Scholar

[15]

S. C. Matz and R. J. P. de Figueiredo, A nonlinear image contrast sharpening approach based on munsell's scale, IEEE Transactions on Image Processing, 15 (2016), 900-909.   Google Scholar

[16]

M. Nikolova, A fast algorithm for exact histogram specification. simple extension to colour images, Scale Space and Variational Methods in Computer Vision, 2013,174–185. Google Scholar

[17]

M. Nikolova and G. Steidl, Fast hue and range preserving histogram specification: Theory and new algorithms for color image enhancement, IEEE Transactions on Image Processing, 23 (2014), 4087-4100.  doi: 10.1109/TIP.2014.2337755.  Google Scholar

[18]

M. Nikolova and G. Steidl, Fast ordering algorithm for exact histogram specification, IEEE Transactions on Image Processing, 23 (2014), 5274-5283.  doi: 10.1109/TIP.2014.2364119.  Google Scholar

[19]

M. Nikolova, Y. Wen and R. Chan, Exact histogram specification for digital images using a variational approach, Journal of Mathematical Imaging and Vision, 46 (2013), 309-325.  doi: 10.1007/s10851-012-0401-8.  Google Scholar

[20]

M. K. Ng and W. Wang, A total variation model for retinex, SIAM J. Imaging Sciences, 4 (2011), 345-365.  doi: 10.1137/100806588.  Google Scholar

[21]

F. Pierre, J. F. Aujol, A. Bugeau, G. Steidl and V. T. Ta, Variational contrast enhancement of gray-scale and RGB images, Journal of Mathematical Imaging and Vision, 57 (2017), 99-116.  doi: 10.1007/s10851-016-0670-8.  Google Scholar

[22]

F. Pierre, J. F. Aujol, A. Bugeau, G. Steidl and V. T. Ta, Hue-preserving perceptual contrast enhancement, Image Processing (ICIP), IEEE International Conference on, 2016, 4067–4071. Google Scholar

[23]

A. Polesel, G. Ramponi and V. Mathews, Image enhancement via adaptive unsharp masking, IEEE Trans. Image Process., 9 (2000), 505-510.   Google Scholar

[24]

E. Provenzi and V. Caselles, A wavelet perspective on variational perceptually-inspired color enhancement, International Journal of Computer Vision, 106 (2014), 153-171.  doi: 10.1007/s11263-013-0651-y.  Google Scholar

[25]

A. Rizzi, C. Gatta and D. Marini, A new algorithm for unsupervised global and local color correction, Pattern Recognition Letters, 24 (2003), 1663-1677.   Google Scholar

[26] J. C. Russ, The Image Processing Handbook, Fifth edition. CRC Press, Boca Raton, FL, 2007.   Google Scholar
[27]

C. E. Shannon, A mathematical theory of communication, Bell System Technical Jornal, 27 (1948), 379-423.  doi: 10.1002/j.1538-7305.1948.tb01338.x.  Google Scholar

[28]

R. Sherrier and G. Johnson, Regionally adaptive histogram equalization of the chest, IEEE Trans. Med. Imag., MI-6 (1987), 1-7.   Google Scholar

[29]

J. F. Shi and Y. Cai, A novel image enhancement method using local Gamma correction with three-level thresholding, Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, 2011,374–378. Google Scholar

[30]

Y. H. Shi, J. F. Yang and R. B. Wu, Reducing illumination based on nonlinear Gamma correction, Proceedings of the IEEE International Conference on Image Processing, (2007), 529–532. Google Scholar

[31]

J. Tang, X. Liu and Q. Sun, A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms, IEEE J.Sel. Topics Signal Process., 3 (2009), 74-80.   Google Scholar

[32]

Z. Wang, A. Bovik, H. Sheikh and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. on Image Processing, 13 (2004), 600–612. Google Scholar

[33]

W. Wang, C. Chen, M. K. Ng, An Image Pixel Based Variational Model for Histogram Equalization, Journal of Visual Communication and Image Representation, vol. 34, pp. 118-134, January 2016. Google Scholar

[34]

W. Wang and M. K. Ng, A variational histogram equalization method for image contrast enhancement, SIAM J. Imaging Sciences, 6 (2013), 1823-1849.  doi: 10.1137/130909196.  Google Scholar

[35]

G. Xu, J. Su and H. D. Pan, An image enhancement method based on Gamma correction, Proceedings of the 2nd International Symposium on Computational Intelligence and Design, 2009, 60–63. Google Scholar

Figure 1.  From left to right: the input image; the zooming part; the enhanced results by using LRC-AGC; the zooming part; the enhanced results by using GRC-AGC; the zooming part
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  First row (from left to right): the input image; the enhanced results by setting $ \alpha_1 = 1000 $, and $ \alpha_2 = 10,100, 1000, 10000 $; Second row: the enhanced results by setting $ \alpha_2 = 1000 $, and $ \alpha_1 = 10,100, 1000, 10000 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  First row (from left to right): the input low contrast image; the enhanced results by using GC with $ \gamma = 1/2.2 $; the enhanced results by using GC with $ \gamma = 1/5 $; the enhanced results by using GRC-AGC; Second row: the enhanced results by using LRC-AGC; the enhanced results by using GHE; the enhanced results by using the proposed model. The corresponding zooming parts are displayed in the last two rows respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  First row (from left to right): the input low contrast image; the enhanced results by using GC with $\gamma = 1/2.2$; the enhanced results by using GC with $\gamma = 1/5$; the enhanced results by using GRC-AGC; Second row: the enhanced results by using LRC-AGC; the enhanced results by using GHE; the enhanced results by using the proposed model. The corresponding zooming parts are displayed in the last two rows respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  First row (from left to right): the input low contrast image; the enhanced results by using GC with $\gamma = 1/2.2$; the enhanced results by using GC with $\gamma = 1/5$; the enhanced results by using GRC-AGC; Second row: the enhanced results by using LRC-AGC; the enhanced results by using GHE; the enhanced results by using the proposed model. The corresponding zooming parts are displayed in the last two rows respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  First row (from left to right): the input low contrast image; the enhanced results by using GC with $ \gamma = 1/2.2 $; the enhanced results by using GC with $ \gamma = 1/5 $; the enhanced results by using GRC-AGC; Second row: the enhanced results by using LRC-AGC; the enhanced results by using GHE; the enhanced results by using the proposed model. The corresponding zooming parts are displayed in the last two rows respectively
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  The ground-truth image, the low contrast image, and the enhanced results by using different methods
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  The ground-truth image, the low contrast image, and the enhanced results by using different methods
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  ALC and DE values for enhanced results with different values of $ \alpha_1 $ and $ \alpha_2 $
$ \alpha_2 = 10 $ $ 100 $ $ 1000 $ $ 10000 $ $ \alpha_1 = 10 $ $ 100 $ $ 1000 $ $ 10000 $
ALC 0.0770 0.0625 0.0569 0.0562 0.0659 0.0630 0.0569 0.0537
DE 7.8509 7.7954 7.7525 7.7454 7.7326 7.7525 7.7525 7.7228
Table Options

Download as excel

$ \alpha_2 = 10 $ $ 100 $ $ 1000 $ $ 10000 $ $ \alpha_1 = 10 $ $ 100 $ $ 1000 $ $ 10000 $
ALC 0.0770 0.0625 0.0569 0.0562 0.0659 0.0630 0.0569 0.0537
DE 7.8509 7.7954 7.7525 7.7454 7.7326 7.7525 7.7525 7.7228
Table 2.  ALC and DE values for enhanced results of different models
measuresfigureGC1GC2GRC-AGCLRC-AGCGHEProposed
ALC30.04020.04750.08050.14110.05400.0622
40.03000.03830.05360.08070.07510.0401
50.00520.00530.05110.03980.03750.0189
60.23150.26100.34380.25690.26430.2488
DE36.02975.56546.00677.82255.99287.4358
46.07465.47816.14077.78336.12307.6986
56.81305.97226.94517.52267.19817.5682
64.94584.12804.50594.90255.31315.2316
Table Options

Download as excel

measuresfigureGC1GC2GRC-AGCLRC-AGCGHEProposed
ALC30.04020.04750.08050.14110.05400.0622
40.03000.03830.05360.08070.07510.0401
50.00520.00530.05110.03980.03750.0189
60.23150.26100.34380.25690.26430.2488
DE36.02975.56546.00677.82255.99287.4358
46.07465.47816.14077.78336.12307.6986
56.81305.97226.94517.52267.19817.5682
64.94584.12804.50594.90255.31315.2316
Table 3.  SSIM and PSNR values of the enhanced results by using different methods
measuresFigureGC1GC2GRC-AGCLRC-AGCGHEProposed
7a0.68230.52140.83700.75590.83720.8983
7b0.77240.52020.92050.78060.84140.9609
SSIM7c0.78760.51680.92680.77690.84320.9408
8a0.81890.61880.96990.84590.79270.9837
8b0.83560.67460.92800.62130.87590.9559
8c0.83510.62400.95110.73400.90060.9668
7a12.807212.564820.984415.418118.277522.8673
7b16.447511.052623.312216.429018.563126.2987
PSNR7c17.41089.938221.145416.048618.710121.2444
8a15.579810.947726.640021.158316.766331.3392
8b16.728611.062022.841614.684022.862629.2247
8c18.556512.635023.786517.307420.993826.8890
Table Options

Download as excel

measuresFigureGC1GC2GRC-AGCLRC-AGCGHEProposed
7a0.68230.52140.83700.75590.83720.8983
7b0.77240.52020.92050.78060.84140.9609
SSIM7c0.78760.51680.92680.77690.84320.9408
8a0.81890.61880.96990.84590.79270.9837
8b0.83560.67460.92800.62130.87590.9559
8c0.83510.62400.95110.73400.90060.9668
7a12.807212.564820.984415.418118.277522.8673
7b16.447511.052623.312216.429018.563126.2987
PSNR7c17.41089.938221.145416.048618.710121.2444
8a15.579810.947726.640021.158316.766331.3392
8b16.728611.062022.841614.684022.862629.2247
8c18.556512.635023.786517.307420.993826.8890
[1]

Wenzhong Zhu, Huanlong Jiang, Erli Wang, Yani Hou, Lidong Xian, Joyati Debnath. X-ray image global enhancement algorithm in medical image classification. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1297-1309. doi: 10.3934/dcdss.2019089
[2]

Xuefeng Zhang, Hui Yan. Image enhancement algorithm using adaptive fractional differential mask technique. Mathematical Foundations of Computing, 2019, 2 (4) : 347-359. doi: 10.3934/mfc.2019022
[3]

Lori Badea, Marius Cocou. Approximation results and subspace correction algorithms for implicit variational inequalities. Discrete & Continuous Dynamical Systems - S, 2013, 6 (6) : 1507-1524. doi: 10.3934/dcdss.2013.6.1507
[4]

Elena Beretta, Markus Grasmair, Monika Muszkieta, Otmar Scherzer. A variational algorithm for the detection of line segments. Inverse Problems & Imaging, 2014, 8 (2) : 389-408. doi: 10.3934/ipi.2014.8.389
[5]

Zhi-Feng Pang, Yu-Fei Yang. Semismooth Newton method for minimization of the LLT model. Inverse Problems & Imaging, 2009, 3 (4) : 677-691. doi: 10.3934/ipi.2009.3.677
[6]

Yuan Shen, Xin Liu. An alternating minimization method for matrix completion problems. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020103
[7]

Shui-Nee Chow, Ke Yin, Hao-Min Zhou, Ali Behrooz. Solving inverse source problems by the Orthogonal Solution and Kernel Correction Algorithm (OSKCA) with applications in fluorescence tomography. Inverse Problems & Imaging, 2014, 8 (1) : 79-102. doi: 10.3934/ipi.2014.8.79
[8]

Sanming Liu, Zhijie Wang, Chongyang Liu. Proximal iterative Gaussian smoothing algorithm for a class of nonsmooth convex minimization problems. Numerical Algebra, Control & Optimization, 2015, 5 (1) : 79-89. doi: 10.3934/naco.2015.5.79
[9]

M. Montaz Ali. A recursive topographical differential evolution algorithm for potential energy minimization. Journal of Industrial & Management Optimization, 2010, 6 (1) : 29-46. doi: 10.3934/jimo.2010.6.29
[10]

Jie Huang, Xiaoping Yang, Yunmei Chen. A fast algorithm for global minimization of maximum likelihood based on ultrasound image segmentation. Inverse Problems & Imaging, 2011, 5 (3) : 645-657. doi: 10.3934/ipi.2011.5.645
[11]

Jie Sun, Honglei Xu, Min Zhang. A new interpretation of the progressive hedging algorithm for multistage stochastic minimization problems. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-8. doi: 10.3934/jimo.2019022
[12]

Hyeuknam Kwon, Yoon Mo Jung, Jaeseok Park, Jin Keun Seo. A new computer-aided method for detecting brain metastases on contrast-enhanced MR images. Inverse Problems & Imaging, 2014, 8 (2) : 491-505. doi: 10.3934/ipi.2014.8.491
[13]

Zhengshan Dong, Jianli Chen, Wenxing Zhu. Homotopy method for matrix rank minimization based on the matrix hard thresholding method. Numerical Algebra, Control & Optimization, 2019, 9 (2) : 211-224. doi: 10.3934/naco.2019015
[14]

Qingzhi Yang. The revisit of a projection algorithm with variable steps for variational inequalities. Journal of Industrial & Management Optimization, 2005, 1 (2) : 211-217. doi: 10.3934/jimo.2005.1.211
[15]

Hongxia Yin. An iterative method for general variational inequalities. Journal of Industrial & Management Optimization, 2005, 1 (2) : 201-209. doi: 10.3934/jimo.2005.1.201
[16]

Luchuan Ceng, Qamrul Hasan Ansari, Jen-Chih Yao. Extragradient-projection method for solving constrained convex minimization problems. Numerical Algebra, Control & Optimization, 2011, 1 (3) : 341-359. doi: 10.3934/naco.2011.1.341
[17]

Yu-Ning Yang, Su Zhang. On linear convergence of projected gradient method for a class of affine rank minimization problems. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1507-1519. doi: 10.3934/jimo.2016.12.1507
[18]

Lei Wu, Zhe Sun. A new spectral method for $l_1$-regularized minimization. Inverse Problems & Imaging, 2015, 9 (1) : 257-272. doi: 10.3934/ipi.2015.9.257
[19]

Sandro Zagatti. Minimization of non quasiconvex functionals by integro-extremization method. Discrete & Continuous Dynamical Systems - A, 2008, 21 (2) : 625-641. doi: 10.3934/dcds.2008.21.625
[20]

Tengfei Yan, Qunying Liu, Bowen Dou, Qing Li, Bowen Li. An adaptive dynamic programming method for torque ripple minimization of PMSM. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2019136

2018 Impact Factor: 1.469

Tools

Metrics

  • PDF downloads (90)
  • HTML views (318)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences