Fig. 1. Schematic setup for laser speckle contrast imaging (LSCI)
[30] Fig. 2. Analysis and solution of key technical problems of LSCI
Fig. 3. Scheme of aLSCI algorithm
[98] Fig. 4. Comparative experimental results of different algorithms
[98]. (a) tLSCI algorithm; (b) sLSCI algorithm; (c) stLSCI algorithm; (d) savgtLSCI algorithm; (e) tavgsLSCI algorithm; (f) aLSCI algorithm; (g) contrast-to-noise ratio (CNR) of different algorithms
Fig. 5. LSCI filtering model based on eigenvalue-decomposition
[64] (
: original speckle signal vector;
: speckle signal vector after denoising;
: static scattered light signal;
: fluctuating blood signal;
: white noise signal)
Fig. 6. LSCI filtering algorithm based on eigenvalue-decomposition and filtering
[100] Fig. 7. Comparative experimental results
[100]. (a) Raw fundus contrast image; (b) fundus contrast image after eigenvalue-decomposition and spatial filtering
Fig. 8. Scheme of MD-ABM3D algorithm
[47] Fig. 9. Output of different denoising algorithms
[47]. (a) Original image, where PSNR is 18.5, MSSIM is 0.46, and
R is 0.813; (b) savg-tLSCI algorithm, where PSNR is 32.8, MSSIM is 0.87, and
R is 0.987; (c) NLM algorithm, PSNR is 31.0, MSSIM is 0.90, and
R is 0.986; (d) BM3D algorithm, PSNR is 35.8, MSSIM is 0.92, and
R is 0.993; (e) MD-ABM3D algorithm, PSNR is 37.8, MSSIM is 0.96, and
R is 0.996; (f) reference image
Fig. 10. Model of rLASCA algorithm
[61] Fig. 11. Experimental results of rLASCA algorithm
[61]. (a) Unregistered laser speckle contrast image; (b) laser speckle contrast image registered by rLASCA; (c) enlarged image of white rectangular box area in figure (a); (d) enlarged image of white rectangular box area in figure (b); (e) white light map of white rectangular box area
Fig. 12. Non-rigid registration algorithm based on non-coherent light
[45]. (a) Experimental setup of dual-mode lighting system; (b) algorithm model
Fig. 13. Comparison of rigid registration and non-rigid registration
[45]. (a) Unregistered blood flow image; (b) blood flow image after rigid registration; (c) blood flow image after non-rigid registration
Fig. 14. Correction model for LSCI movement artifact based on image decomposition
[106]. (a) Correction model; (b) selection of regression variance; (c) fitted by regression analysis; (d)-(f) contrast value before and after movement correction
Fig. 15. LSCI correction model based on contourlet transform and multi-focus image fusion
[46] Fig. 16. Experiment results before and after nonuniform intensity correction
[103]. (a) Contrast image affected by nonuniformity; (b) reconstructed contrast image
Fig. 17. Experimental results of nonuniform correction
[110]. (a) Grayscale speckle images at two different intensities; (b) from the top to the bottom: contrast maps at high intensity and low intensity and corrected contrast map at low intensity; (c) contrast profile along the red line marked in figure (a) of contrast maps at low intensity and high intensity and corrected contrast map at low intensity; (d) contrast profile along yellow line marked in figure (a) of corrected contrast map at low intensity
Fig. 18. Blood flow image processed by dLSI algorithm
[84] Fig. 19. Schematic of multi-focus imaging setup
[119] Fig. 20. Model of dynamic scattering contrast correction model
[74] Fig. 21. Spatial frequency domain imaging
LSCI
[121]. (a) Experimental setup of si-SFDI; (b) processing flow of si-SFDI
Fig. 22. Experimental setup for optical speckle image velocimetry (OSIV)
[10] Fig. 23. Processing flow of OSIV algorithm
[10] Fig. 24. Sample entropy-based laser speckle contrast analysis method and partial experimental results
[111]. (a) Sample entropy-based laser speckle contrast analysis method; (b) partial experimental results
Fig. 25. Multi-exposure laser speckle imaging
[83]. (a) Multi-exposure speckle imaging system; (b) percentage deviation in
under single exposure model and MESI
Fig. 26. Lateral speckle contrast analysis method combined with non-wide field illumination
[127]. (a) Schematic of LSCI experimental setup based on line beam scanning illumination; (b) image processing flow; (c)-(d) blood flow images obtained by traditional contrast analysis method, lateral speckle contrast analysis methods weighted with constant and depth sensitivity curves, respectively
Fig. 27. Schematic of DSCA imaging system
[132] Fig. 28. LSCI system for blood flow
[130]. (a) TR-LSCI system; (b) conventional reflective-detected LSCI system
Fig. 29. Novel LSCI systems and their advances in application and research
Fig. 30. Portable LSCI based on DSP
[135]. (a) Schematic illustration of portable LSCI system; (b) block diagram of hardware framework; (c) block diagram of software framework
Fig. 31. Portable LSCI based on FPGA
[136] Fig. 32. Efficient portable LSCI based on embedded GPU
[57] Fig. 33. Endoscopic LSCI system
[50,88] Fig. 34. Dual-display laparoscopic laser speckle contrast imaging (LSCI) system
[14]. (a) Laparoscopic LSCI system; (b) inserted laparoscopy; (c) handheld operation; (d) LSCI bowel imaging; (e) LSCI gallbladder imaging; (e) LSCI mesentery imaging
Fig. 35. Head-mounted LSCI
[60] Fig. 36. Schematic of ECoG-LSCI
[23] Fig. 37. Speckle contrast images for rCBF upon electrical stimulation in forelimb- and hindlimb-stimulated groups at serial time points
[23] Fig. 38. Multimodal and functional imaging of retina
[17] Fig. 39. Multimodal system for real-time surgical guidance
[141] Scattering regime | Velocity distribution | Speckle visibility expression |
---|
Single | Lorentzian | | Multiple | Gaussian | |
|
Table 1. Correction model of dynamic speckle contrast
[115] | Scattering regime | Motion | Vessel size | Notation |
---|
| Multiple | Unordered | Small(diameter is about less than | n=0.5 for MU | | Multiple | Ordered | Medium (diameter is about 30-110 m) | n=1 for MO or SU | Single | Unordered | | Single | Ordered | Large(diameter is about more than ) | n=2 for SO |
|
Table 2. Electric field autocorrelation function
for different scattering characteristics and particle motion models
[73]