To measure large and heavy micro-structured workpieces in situ and improve the measurement accuracy, which strongly depends on the environment during the measurement of micro-structured surfaces, a workpiece rotating technique is proposed. This method utilizes the precise rotation of machine tools to drive the workpiece and records a set of elemental image arrays for the pickup stage to overcome the upper-resolution limit imposed by the Nyquist sampling theorem, which allows the increase in the two-dimensional spatial resolution of the computed depth images in integral imaging. By extracting the depth position, we can obtain accurate depth information and measurement results for micro-structured surfaces. We carried out simulations and experiments to demonstrate the proposed method, and the results show the feasibility of our method.
To obtain satisfactory performance in characterizing optical freeform surfaces with local features, this paper proposes a model of a radial basis function with slope-based shape factor and distribution (RBF-SSD). Compared to previous RBF-slope models with only slope-based shape factors, the RBF-SSD model relates both shape factors and distribution with the surface slope, ensuring greater fitting ability can be achieved when fitting a surface with local features. Fitting experiments for two different surfaces demonstrated the fitting ability of the RBF-SSD model. An off-axis three-mirror system with 3 ° × 3.6 ° field of view was designed as an example to show the optical design efficacy of our model.
The study of imaging through scattering media especially 3D imaging is of great significance in many fields such as biomedical imaging. Recently, deep learning has been widely used in the field of information processing with its remarkable performance. In this paper, we proposed a method of three - dimensional imaging through scattering media based on deep learning. This method uses the deep neural network to process the information captured by the light field imaging system based on the microlens array, recovering the no-scattering 4D light field information, and then realize three-dimensional reconstruction by using the processed light field information. Deep learning method requires a large number of samples. But in many environments, it is difficult to obtain a large number of three-dimensional samples through experiment. To solve this crucial problem, we use incoherent light propagation model to simulate the light field propagation and generate samples which contains three-dimensional information through simulation. In this paper, we simulated the propagation of radiation emitted from objects behind a single layer of weak scattering media, generated a large number of samples of 4D light field information by simulation, trained the neural network and processed the test data set generated by simulation, and we realized the deblurring of the light field information which contains information of multiple layers of flat semitransparent objects, which could be used to realize the 3D reconstruction.
It is of great importance to utilize a model to characterize the surfaces while designing an optical imaging system with freeform surfaces. For this purpose, a model with radial basis function based on surface slope (RBF-slope) for optical freeform surfaces was investigated by establishing the relationship between the shape factor and local surface slope, and improving the distributions of the basis functions for circular apertures. We performed the fitting experiment for “bumpy” paraboloids; the results demonstrated that the RBF-slope model has stronger fitting ability than conventional RBF model. A prototype of single mirror magnifier for head-worn display was designed and fabricated, in which the freeform mirror was described and characterized using the RBF-slope model. It can be proved by the design results that the RBF-slope model for optical freeform surfaces has obvious advantages in aberration balancing over conventional model. The primary experiments showed that the freeform surface was well fabricated and expected image display can be achieved.
In laser speckle imaging methods, speckle correlation coefficient analysis (SCCA) shows great potential in blood flow monitoring because of its high sensitivity to slow flows than laser speckle contrast imaging (LSCI). But the published SCCA usually depends on short exposure times and high frame rates to preserve the autocorrelation. Differently, in this paper we use SCCA at normal exposure time (20 ms) to evaluate the performance of skin optical clearing (SOC) and demonstrate that SCCA with long exposure time also significantly improves the ability to image large range of flow speeds over LSCI.
In the field of imaging through a single multimode fiber (MMF), various methods have been explored to reconstruct the object. In all these methods, deep learning is more robust and the imaging system is less complex. In this paper, we verified the reconstruction of images from a few-mode fiber with deep learning methods based on the intensity of the light filed with simulation and experiments. Compared with another broadly used method that depends on the measurement of optical fiber transmission matrix, the reconstructed images with deep learning method are not affected by the circular field of view of the fiber aperture.
A reconstruction method of axially moving lenslet array (AMLA) was proposed. First, the dot-pattern reconstructed image was generated from a set of AMLA-recorded EIs using pixel-to-pixel mapping. Then, the empty pixels of dot-pattern reconstructed image can be filled by the average of the intensity values of all pixels that are corresponding to the empty pixels in each EI and determined through back projection. Using this proposed method, not only can the empty pixels be eliminated, but also the image quality is higher than conventional interpolation. Moreover, the axially moving gap can also be arbitrary, which breaks the limitation of the strict interpolation-free condition of the conventional reconstruction method. The feasibility of this method was verified by simulations and optical experiments.
We present a dual wavelength endoscope which uses Endoscopic Laser Speckle Contrast Analysis (ELASCA) with the aim to image tissue blood flow and perfusion during surgical procedures. In this study we measure speckle decorelation times, which are associated with flow, by imaging speckle patterns at a wide range of detector exposure times. In order to understand the effects of image collection efficiency and sample scattering properties, control experiments with different optical systems were performed by imaging of tissue mimicking phantoms and inferring their flow parameters.
In this paper we present a method to visualize the pressure field of an ultrasound beam in a single shot of the CCD and
to image the shear wave propagation based on acousto-optic laser speckle contrast analysis. The contrast images show
features in the near field, far field and central region of the ultrasound beam and the pressure profile fits with that
measured with a hydrophone. The shear wave propagation was acquired by changing the imaging delay time after the
ultrasound burst. This method can be used to study the shear wave properties of common tissue phantoms to guide
experiments on tissue.
We present a dual-wavelength endoscopic laser speckle contrast system including illumination with polarization
maintaining fibres and imaging using a leached fibre image guide. This system has a frame rate of 10 Hz and can
rapidly monitor changes in blood flow in vivo, including due to the heart beat, using the contrast values of the speckle
images recorded with 1 ms exposure time. In addition the mean intensities can record the respiration period and can indicate changes in tissue oxygenation. This system was tested during an occlusion to a human finger and is being applied in endoscopy.
There are several challenges when fibre image guides (FIG) are used for endoscopic speckle acquisition: cross talk
between fibre cores, FIG fixed pattern noise, the small probe diameter and low sensitivity and resolution due to the
decreased number of speckles and their low transmission through the FIG. In this paper, an endoscopic laser speckle
contrast analysis system (ELASCA) based on a leached fibre image guide (LFIG) is presented. Different methods of
acquiring LASCA images through LFIGs were investigated including the effect of changing the number of speckles per
fibre, defocusing the FIG image onto the CCD and processing speckle images with masks and Butterworth filters to deal
with the LFIG fixed pattern and noise from the cladding. The experimental results based on a phantom consisting of
intralipid suspension pumped at varying speed showed that this system could detect speed changes and that in the case of
multiple speckles per fibre the Nyquist frequency criterion need not be applied since the speckle may be transferred
through the fibres to some extent. In contrast to the previously reported ELASCA results, this system can both give a
map of the observed area and the temporal change in flow. An additional benefit is the small size of the LFIG, which is
compatible with current endoscopic instrument channels and may allow additional surgical applications.
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