Fig. 1. Schematic of the digital holographic record

Fig. 2. Sample simulation scenario obtained under different conditions. (a) Ideal case; (b) a case with Gaussian noise variance of 0.0005; (c) a case with Gaussian noise variance of 0.001

Fig. 3. Simulation holograms obtained under different conditions. (a) Ideal case; (b) a case with Gaussian noise variance of 0.0005; (c) a case with Gaussian noise variance of 0.001

Fig. 4. Schematic outline of simulation system

Fig. 5. Reconstruction results for different iteration times when τ=0.1. (a) t=50; (b) t=100; (c) t=150; (d) t=200; (e) t=250; (f) t=300; (g) t=350; (h) t=400

Fig. 6. Relationship between number of iteration and (a) PSNR or (b) MSSIM under ideal condition

Fig. 7. Relationship between sparse restriction parameter and (a) PSNR or (b) MSSIM under ideal condition

Fig. 8. Ideal reconstructed images. (a) “T” sample; (b) “H” sample

Fig. 9. Relationship between sparse restriction parameter and (a) PSNR or (b) MSSIM when Gaussian noise variance is 0.0005

Fig. 10. Relationship between sparse restriction parameter (0.05≤τ≤0.14) and (a) PSNR or (b) MSSIM when Gaussian noise variance is 0.0005

Fig. 11. Relationship between number of iteration and (a) PSNR or (b) MSSIM when Gaussian noise variance is 0.0005

Fig. 12. The best reconstructed images with Gaussian noise variance of 0.0005. (a) “T” sample; (b) “H” sample

Fig. 13. Relationship between sparse restriction parameter and (a) PSNR or (b) MSSIM when Gaussian noise variance is 0.0001

Fig. 14. Relationship between number of iteration values and (a) PSNR or (b) MSSIM when Gaussian noise variance is 0.001

Fig. 15. The best reconstructed images with Gaussian noise variance of 0.001. (a) “T” sample; (b) “H” sample

Fig. 16. Reality hologram reconstruction. (a) τ=0.02; (b) τ=0.1