3. Experiments and results
3.2 Improved lateral resolution imaging of microstructure samples
Thus far, we have successfully demonstrated the effective lateral resolution and image quality improvement by the multi-frame superresolution processing with shifted low resolution C-scans. This processing can offer better image quality with
Figure 12.
The OCT lateral images of the resolution target in Figure 9(b) were taken by a 30 mm focal length achromatic lens using (A) 6464 scan matrix, (B) 20482048 scan matrix, (C) 10241024 scan matrix with 5 frame averaging, and (D) the superresolution and blind deconvolution processed image from 961 shifted 6464 low resolution images with 1/16-spot-spacing step and maximum 15/16-spot-shift. The estimated PSF is attached at the right bottom of (D). All the four images have the same FOV of about 250250μm2.
less scan time than high density C-scan images and is especially suitable for imaging micron scale fine structures [19, 37, 71–74].
We examined 3D imaging of a microstructure sample in Figure 13, in which the particle size is about 3μm. The two left images of Figure 13(A) and (B) are the original sparse scan lateral SVP images of the same sample using 30 and 19 mm focal length lens with 13001300 and 500500μm2lateral FOV, respectively. Even with 19 mm focal length lens and4μm focused spot size, the microstructures are still invisible. After superresolution processing of 225 low resolution shifted frames with 1/8-spot-spacing step and maximum 7/8-spot-shift (using 1515 shift matrix with arrangement similar to Figure 7(d)), we are able to observe clearly those microstructures and wavy surface caused by the imperfect fabrication in exposure and developing. This wavy surface is difficult to be seen in microscope imaging without topographic imaging capability. As our previous report, the multiple-frame superresolution processing can improve the lateral resolution of our SD-OCT with 19 mm focal length lens by3 times, achieving 1–2μm [37]. Although 19 mm focal
Figure 13.
Lateral SVP imaging of a microstructure sample without (left) and with (right) multi-frame superresolution processing. (A) 30 mm and (B) 19 mm focal length lens are used in the sample arm of our SD-OCT system.
Except the ability of observation of3μm particles, the right images show wavy surface with various imperfect fabrications, which is difficult to be observed in microscopy imaging. The scale bars in (A) and (B) are 100μm.
Practically, it is difficult to obtain a penetrated lateral image with so smooth back- ground as well as maintaining high resolution due to various scattering mediums in the samples.
When a focused beam penetrating into a sample, the scattering would alter the cross-section profile of the beam. The optimized lateral PSF thus may be different in different samples and at different depth layers [45]. Even with advanced blind deconvolution, the ground true PSF [70] of the system at that depth layer is still difficult to find. We also could notice that the estimated PSFs in Figures 10(J), 11 (F) and 12(D) are different.
Considering the above issues, we would not apply the deconvolution processing to the following OCT experiments of functional samples. However, it is important to point out that the superresolution technique with deconvolution processing can break the diffraction limit, improve the sampling rate and suppress the background noise together to significantly improve the lateral resolution and image quality.
3.2 Improved lateral resolution imaging of microstructure samples
Thus far, we have successfully demonstrated the effective lateral resolution and image quality improvement by the multi-frame superresolution processing with shifted low resolution C-scans. This processing can offer better image quality with
Figure 12.
The OCT lateral images of the resolution target in Figure 9(b) were taken by a 30 mm focal length achromatic lens using (A) 6464 scan matrix, (B) 20482048 scan matrix, (C) 10241024 scan matrix with 5 frame averaging, and (D) the superresolution and blind deconvolution processed image from 961 shifted 6464 low resolution images with 1/16-spot-spacing step and maximum 15/16-spot-shift. The estimated PSF is attached at the right bottom of (D). All the four images have the same FOV of about 250250μm2.
less scan time than high density C-scan images and is especially suitable for imaging micron scale fine structures [19, 37, 71–74].
We examined 3D imaging of a microstructure sample in Figure 13, in which the particle size is about 3μm. The two left images of Figure 13(A) and (B) are the original sparse scan lateral SVP images of the same sample using 30 and 19 mm focal length lens with 13001300 and 500500μm2lateral FOV, respectively. Even with 19 mm focal length lens and4μm focused spot size, the microstructures are still invisible. After superresolution processing of 225 low resolution shifted frames with 1/8-spot-spacing step and maximum 7/8-spot-shift (using 1515 shift matrix with arrangement similar to Figure 7(d)), we are able to observe clearly those microstructures and wavy surface caused by the imperfect fabrication in exposure and developing. This wavy surface is difficult to be seen in microscope imaging without topographic imaging capability. As our previous report, the multiple-frame superresolution processing can improve the lateral resolution of our SD-OCT with 19 mm focal length lens by3 times, achieving 1–2μm [37]. Although 19 mm focal
Figure 13.
Lateral SVP imaging of a microstructure sample without (left) and with (right) multi-frame superresolution processing. (A) 30 mm and (B) 19 mm focal length lens are used in the sample arm of our SD-OCT system.
Except the ability of observation of3μm particles, the right images show wavy surface with various imperfect fabrications, which is difficult to be observed in microscopy imaging. The scale bars in (A) and (B) are 100μm.
length lens can provide better image resolution than 30 mm focal length lens due to smaller focal spot, it sacrifices the lateral FOV and axial DOF of the system. This trade-off should be considered when imaging different samples. In this experiment, the superresolution enhanced 30 mm focal lens system has provided good enough resolution ability to exhibit the details of the sample.
Except for better human vision, the lateral resolution and image quality improvements further benefit various machine vision algorithms, providing more details for feature detection. Our previous work has reported the superresolution assisted image stitching for achieving an ultra-wide lateral FOV. Taking Figure 14 as an example, we scanned a multi-layer microfluidic sample by the high density scan and our multi-frame superresolution with shifted C-scans introduced above.
All the structures are visible in Figure 14(A) left, however with a lot of speckle noises. Applying the advanced SURF [75] feature detection algorithm to the left two adjacent SVP images, there are no correct feature pairs found between them. And the incorrect matching information fails the following image stitching, overlapping two left images as Figure 14(A) right. Actually, there is only 30% shared region for the left two images. This failure is because most machine vision algorithms are not robust to periodic structures and noisy background. After superresolution
processing, the image quality is significantly improved as in Figure 14(B) left, although with the same pixel resolution. The improved images offer much more correct feature pairs, supporting the following image stitching algorithm to recon- struct a wide lateral FOV image successfully at right. This comparison demonstrates the superresolution technique would be an effective pre-processing for subsequent machine vision algorithms.
As we discussed in Section 2.1, each lens has its lateral FOV limitation due to Petzval field curvature. For example, 14001400μm2optimized lateral FOV for 30 mm focal length lens guarantees the overall high resolution for the whole C-scan region. However, this lateral FOV is obviously not enough to image a large sample with centimeter scale sizes. To overcome this drawback, we scan 6 nearby partial overlapped regions of a microstructure sample by a 30 mm focal length lens. Each local C-scan covers a FOV of 13001300μm2and is enhanced by the
Figure 14.
(A) Failed image stitching from two noisy images. (B) The superresolution processed images provide more correct matched feature pairs and successfully help the subsequent stitching algorithm to reconstruct a wide FOV image.
superresolution processing. One of the SVP images is shown in Figure 13 (A) right.
After repeating the image stitching layer by layer introduced in our previous work [37, 38], we generated a 3.22.3 mm2wide FOV seamless 3D image with high lateral resolution, as shown in Figure 15(A). Wide FOV images at three selected depth layers are shown in Figure 15(B). If enlarge the selected two B-scans (posi- tions of the two arrows in the top view) in Figure 15(C), all adjacent parts are also stitched very well without any discontinuities. The details of the image stitching are given in our papers [37, 38].
Again, we stitched 10 close-by C-scans with 500500μm2FOV, imaged by a 19 mm focal length lens, to reproduce a 2.101.15 mm2wide FOV 3D image in Figure 16. Due to short focal length lens, this figure stitched by more C-scans only covers 1/3 area of Figure 15, although with higher lateral resolution of<2μm
Figure 15.
(A) Top view of a 3.22.3 mm2microstructure image stitched by six 13001300μm2images. (B) Selected three depth layers in the stitched high lateral resolution 3D image. (C) Two B-scans in the stitched 3D image are selected by the arrows in (A).
length lens can provide better image resolution than 30 mm focal length lens due to smaller focal spot, it sacrifices the lateral FOV and axial DOF of the system. This trade-off should be considered when imaging different samples. In this experiment, the superresolution enhanced 30 mm focal lens system has provided good enough resolution ability to exhibit the details of the sample.
Except for better human vision, the lateral resolution and image quality improvements further benefit various machine vision algorithms, providing more details for feature detection. Our previous work has reported the superresolution assisted image stitching for achieving an ultra-wide lateral FOV. Taking Figure 14 as an example, we scanned a multi-layer microfluidic sample by the high density scan and our multi-frame superresolution with shifted C-scans introduced above.
All the structures are visible in Figure 14(A) left, however with a lot of speckle noises. Applying the advanced SURF [75] feature detection algorithm to the left two adjacent SVP images, there are no correct feature pairs found between them. And the incorrect matching information fails the following image stitching, overlapping two left images as Figure 14(A) right. Actually, there is only 30% shared region for the left two images. This failure is because most machine vision algorithms are not robust to periodic structures and noisy background. After superresolution
processing, the image quality is significantly improved as in Figure 14(B) left, although with the same pixel resolution. The improved images offer much more correct feature pairs, supporting the following image stitching algorithm to recon- struct a wide lateral FOV image successfully at right. This comparison demonstrates the superresolution technique would be an effective pre-processing for subsequent machine vision algorithms.
As we discussed in Section 2.1, each lens has its lateral FOV limitation due to Petzval field curvature. For example, 14001400μm2optimized lateral FOV for 30 mm focal length lens guarantees the overall high resolution for the whole C-scan region. However, this lateral FOV is obviously not enough to image a large sample with centimeter scale sizes. To overcome this drawback, we scan 6 nearby partial overlapped regions of a microstructure sample by a 30 mm focal length lens. Each local C-scan covers a FOV of 13001300μm2and is enhanced by the
Figure 14.
(A) Failed image stitching from two noisy images. (B) The superresolution processed images provide more correct matched feature pairs and successfully help the subsequent stitching algorithm to reconstruct a wide FOV image.
superresolution processing. One of the SVP images is shown in Figure 13 (A) right.
After repeating the image stitching layer by layer introduced in our previous work [37, 38], we generated a 3.22.3 mm2wide FOV seamless 3D image with high lateral resolution, as shown in Figure 15(A). Wide FOV images at three selected depth layers are shown in Figure 15(B). If enlarge the selected two B-scans (posi- tions of the two arrows in the top view) in Figure 15(C), all adjacent parts are also stitched very well without any discontinuities. The details of the image stitching are given in our papers [37, 38].
Again, we stitched 10 close-by C-scans with 500500μm2FOV, imaged by a 19 mm focal length lens, to reproduce a 2.101.15 mm2wide FOV 3D image in Figure 16. Due to short focal length lens, this figure stitched by more C-scans only covers 1/3 area of Figure 15, although with higher lateral resolution of<2μm
Figure 15.
(A) Top view of a 3.22.3 mm2microstructure image stitched by six 13001300μm2images. (B) Selected three depth layers in the stitched high lateral resolution 3D image. (C) Two B-scans in the stitched 3D image are selected by the arrows in (A).
[37]. The top view, selected layers, and selected B-scans are exhibited in Figure 16(A)–(C), respectively. The wide FOV images could be enlarged for stitching performance and image quality checking by readers. In principle, there is no limitation on lateral FOV enlargement by this image stitching technique while maintaining the needed high lateral resolution SD-OCT imaging by the multi-frame superresolution processing. While, for fully review the microstructure sample, the 19 mm lens need to image more than 30 adjacent regions due to small lateral FOV, spending at least 5 times more scan time than using a 30 mm focal length lens, thus it is only suitable for ultra-high lateral resolution imaging.
3.3 Improved lateral resolution imaging of in vivo 3D fingerprint