Abstract:Aiming at the high cost of the traditional liquid lens control system and the inability to meet the problems of personalised customisation, a control system for automatic focusing of liquid lenses is developed. The system selects ATmega32U4 microcontroller as the main control chip, LTC2662 chip and H-bridge circuit as the current output module for controlling the liquid lens to achieve zoom, and provides two operation modes, manual and automatic, for users to choose. The current output module of this system can output a stable working current of DC -300~300 mA, with an average error of 0.64% and a current stability of 0.327 7%; and the average time consumed by the auto-focusing of the focus control software is about 1.3 s, with an image resolution of up to 45.3 lp/mm. Compared with the existing liquid lens control system, this system realizes the automatic adjustment of the focus of the liquid lens, which is simple in operation, stable in performance, and low in cost. Compared with the existing liquid lens control system, this system can automatically adjust the focal length of the liquid lens with simple operation, stable performance and lower cost, providing more flexible and personalised customisation options for liquid lenses.

Abstract:Aiming at the problems of low sensitivity and many local extreme points of the clarity evaluation function during the focusing process of the photoelectric imaging system. This paper proposes an image clarity evaluation algorithm based on regional weighting. First, the algorithm adopts a threshold based on the traditional Laplacian evaluation function to improve the anti-noise and the ratio of clarity. Then, the algorithm also uses the image gradient map to calculate the regional clarity weighting factor which can optimize the variance of flat part of focusing curve. Experimental results show that compared with the most traditional clarity evaluation functions, the clarity ratio of this algorithm is increased by about 2.7 times、the sensitivity increases by about 1.9 times and the variance of flat part of focusing curve can be reduced to 1/6 of the traditional Laplacian evaluation function.In general, this algorithm has the advantages of high clarity ratio and sensitivity、low variance of flat part of focusing curve and has better evaluation performance when the image content is complex.

Abstract:In order to realize high-precision real-time detection of large-stroke precision optical focusing components In order to realize high-precision real-time detection of large-stroke precision optical focusing components, high-precision long-displacement sensors based on axial eddy current effect are studied. A long-displacement eddy current probe simulation model is established for linearity testing, an eddy current sensor test system is built for accuracy experiments, and the long-displacement eddy current sensor is connected to the precision optical focusing assembly. The experimental results show that while the measurable displacement reaches 24 mm, the linearity is better than 1%, the resolution is better than 0.5 μm, the accuracy is better than 1 μm, and the high-precision long-displacement eddy current sensor meets the requirements of the precision optical focusing assembly.

Abstract:To improve the fringe projection efficiency and measurement accuracy of the optical 3D measurement system, a binary defocusing method is proposed to rapidly project the coded fringes and the binocular stereo matching algorithm is used to obtain the parallax information. First, the Bayer dithering technology is used to convert the projected grayscale fringes into binary fringes to reduce the effect of the projector′s defocusing on the contrast and depth of field drop. Then, the wrapped phase is expanded into absolute phase by means of multifrequency heterodyne method, disparity constraint relation and average error sum of squares cost calculation are used to obtain the best phase matching point. Finally, the depth information and threedimensional information of the measured object are obtained through the principle of triangulation and dualtarget positioning. Through experiments on precision balls and statues, it is proved that the measurement accuracy of the method proposed in this paper is close to that of the traditional twelve-step phase shift measurement, and is 52.8% higher than the traditional three-step phase shift.

Abstract:In the treatment of thrombotic diseases, extracorporeal ultrasound thrombolysis breaks through the limitations of conventional treatment and in vivo interventional thrombolysis and shows great advantages.However, the ultrasonic transducer used for extracorporeal thrombolysis has low transmitting power and is easily influenced by the environment, which makes it difficult for the ultrasonic signal to reach the lesion area through human tissues and to achieve the destruction of the thrombus structure.In order to solve this problem, a new model of extracorporeal ultrasonic thrombolysis was designed and simulated based on the idea of defective state structure and finite element analysis in this paper.The experimental results show that this structural model can have good acoustic field localization effect and acoustic intensity enhancement effect in the low frequency band part of 10~20 kHz ultrasonic waves, and when the acoustic frequency is rate 14.9 kHz, the structural model has good body penetration effect and has certain possibility to produce structural damage to thrombus in vitro and achieve the effect of thrombus cleaning.These studies are of great importance for the research of efficient and safe in vitro thrombus clearance methods.

Abstract:Aiming at the strong real-time demand of image processing in the focus detection system of projection lithography machine, a focus detection image real-time processing system based on SOPC is designed with FPGA chip as the carrier. Firstly, the MicroBlaze embedded soft core is used to execute the image processing algorithm, and the system scheme verification and algorithm debugging can be carried out without changing the hardware architecture; Then, using the scheme of software and hardware cooperation, the algorithm is transplanted to FPGA hardware to accelerate the focus detection process. The FPGA chip carries out data interaction between software and hardware through Axi bus, and the off chip is interconnected with the worktable through optical fiber interface to complete the high-speed upload of focus detection results. The experimental results show that the system runs stably, and it takes 104 μs to complete the entire focus detection process, and the software-hardware collaboration is about 1700 times faster than the soft-core implementation.

Abstract:The depth of field for imaging equipment is limited, the problem of out-of-focus of part of the acquisition image.An effective multi-focus image fusion algorithm is proposed, to further improve the contrast and sharpness of fused image. Firstly,the source image is decomposed into approximate subbands and detailed subbands by NSCT; Secondly,the FMO and ISML were used to combine the approximate subband coefficients and the detailed subband coefficients respectively;finally, the fused image is obtained by inverse NSCT. Experiments were conducted using a gray scale multi-focus image dataset, and comparative analysis with commonly used multi-focus image fusion algorithm shows that, the proposed fusion algorithmhas superior performance in terms of visual inspection and 7 commonly used objective evaluation indicators.

Abstract:In view of the limited depth of field of the image acquisition sensor, which leads to the out-of-focus phenomenon in the local area of the acquired image, this paper proposes a new multi-focus image fusion algorithm. Under the framework of NSST, the fusion rules based on discrete cosine transform (DCT) and local spatial frequency (LSF) are used for the low-frequency sub-band decomposition coefficients, and the fusion rules based on the maximum and minimum filtering combined with average filtering and median filtering (MMAM) are used for the high-frequency sub-band decomposition coefficients; and then perform INSST reconstruction to obtain fused images. The experimental results show that, compared with the classical image fusion algorithm, the proposed algorithm can effectively fuse the high and low frequency sub-band information of the image, and achieves better results in both subjective and objective evaluation.

Abstract:In this paper, an unsupervised deep learning algorithm is presented to solve the problem of multi-focus fusion of rock slice images collected under a microscope. In order to extract the deep features of images, a codec neural network is trained with unsupervised method to extract the depth features of different focused images and get the feature map. Then, a binary decision graph is calculated using the spatial frequency of the feature graph. Due to subtle decision bias, there may be holes and burrs in the binary decision maps, so the decision maps are morphologically processed and filtered. Finally, the fused image is obtained from the processed decision map. The experimental results show that the data evaluation index 、、 of this method is 0.7477、0.9874、0.7969.At the same time, the subjective effect is better than other methods. Therefore, Experiments show that this method can achieve good results in the application of multi-focus fusion of microscopic rock slice images and general images.

Abstract:Defect detection on outer packaging of wine bottles is of importance. A improved YOLOv3 algorithm is proposed to deal with that problem. The final result shows the improved YOLOv3 meets the production line’s requirements for accuracy and speed well. First, The SENet Module is introduced into YOLOv3 backbone network’s residual block, applying the attention mechanism to enhance the feature extraction. Second, The Adaptive Feature Fusion Network (ASFF) is introduced into the Feature Pyramid Network to fuse the feature information in different scales, which enhance the predictive ability of the model. Third, The Focus Loss function is used to solve the problem of unbalanced positive and negative samples, which will help accelerate the convergence speed of the loss function. The improved YOLOv3-ASFL achieves a mAP up to 92.33% in the self-made wine bottle cap dataset, which is 6.59% higher than the original YOLOv3, and the single image detection time is only 0.085s. The improved YOLOv3 model has a better performance and meets the needs of the wine bottle packaging production line for defect detection.