Research on Spatially Adaptive High-Order Total Variation Model for Weak Fluorescence Image Restoration

1. SHAH S M, CRAWSHAW J P, BOEK E S. Threedimensional imaging of porous media using confocal laser scanning microscopy [J]. Journal of Microscopy, 2017, 265(2): 261–271.

   Article  Google Scholar 

2. SHIMONUKAI T, YOSHIOKA M, YANAGIMOTO H. Estimation of PSF for a shaking blurred image restoration [J]. Electronics and Communications in Japan, 2014, 97(4): 49–56.

   Article  Google Scholar 

3. AGHAZADEH N, BASTANI M, SALKUVEH D K. Generalized Hermitian and skew-Hermitian splitting iterative method for image restoration [J]. Applied Mathematical Modelling, 2015, 39(20): 6126–6138.

   Article  MathSciNet  Google Scholar 

4. CHEONG H, CHAE E, LEE E, et al. Fast image restoration for spatially varying defocus blur of imaging sensor [J]. Sensors, 2015, 15(1): 880–898.

   Article  Google Scholar 

5. KARAKATSANIS N A, TSOUMPAS C, ZAIDI H. Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation [J]. Computerized Medical Imaging and Graphics, 2017, 60: 11–21.

   Article  Google Scholar 

6. INGARAMO M, YORK A G, HOOGENDOORN E, et al. Richardson-Lucy deconvolution as a general tool for combining images with complementary strengths [J]. ChemPhysChem, 2014, 15(4): 794–800.

   Article  Google Scholar 

7. SLAVINE N V, GUILD J, MCCOLL R W, et al. An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study [J]. Physica Medica, 2015, 31(8): 903–911.

   Article  Google Scholar 

8. LAASMAA M, VENDELIN M, PETERSON P. Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images [J]. Journal of Microscopy, 2011, 243(2): 124–140.

   Article  Google Scholar 

9. DEY N, BLANC-FERAUD L, ZIMMER C, et al. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution [J]. Microscopy Research and Technique, 2006, 69(4): 260–266.

   Article  Google Scholar 

10. LIU J, HUANG T Z, SELESNICK I W, et al. Image restoration using total variation with overlapping group sparsity[J]. Information Sciences, 2015, 295: 232–246.

    Article  MathSciNet  MATH  Google Scholar 

11. ZHOU W F, LI Q G. Poisson noise removal scheme based on fourth-order PDE by alternating minimization algorithm [C]//Abstract and Applied Analysis.[s.l.]: Hindawi Publishing Corporation, 2012: 1–14.

    Google Scholar 

12. FIGUEIREDOMA T, BIOUCAS-DIAS J M. Restoration of Poissonian images using alternating direction optimization [J]. IEEE Transactions on Image Processing, 2010, 19(12): 3133–3145.

    Article  MathSciNet  MATH  Google Scholar 

13. KONG Y Y, QIAN X, FANG B, et al. Quality assessment of the deconvolution algorithms for fluorescence images [J]. Beijing Biomedical Engineering, 2014, 33(4): 374–378 (in Chinese).

    Google Scholar 

14. WALLACE W, SCHAEFER L H, SWEDLOW J R. A workingperson’s guide to deconvolution in light microscopy [J]. BioTechniques, 2001, 31(5): 1076–1097.

    Article  Google Scholar 

15. WANG H B, MILLER P C. Scaled heavy-ball acceleration of the Richardson-Lucy algorithm for 3D microscopy image restoration [J]. IEEE Transactions on Image Processing, 2014, 23(2): 848–854.

    Article  MathSciNet  MATH  Google Scholar 

16. CHEN D Q, CHENG L Z. Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring [J]. Inverse Problems, 2011, 28(1): 015004.

    Article  MathSciNet  MATH  Google Scholar 

17. NIELSEN F, SUN K. Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures [J]. IEEE Signal Processing Letters, 2016, 23(11): 1543–1546.

    Article  Google Scholar 

Download references