Focusing Evaluation Function Research Articles

Fast autofocusing in off-axis digital holography based on search region segmentation and dichotomy

Autofocusing is a critical step in digital holography for reconstructing high-quality intensity and phase information, especially for the dynamic detection of moving objects. Some commonly used autofocusing methods in digital holography are time-consuming as they search over a large range to find a unique reconstruction distance. In this study, we propose a new fast autofocusing method based on search region segmentation and dichotomy. First, a new search region is obtained by segmenting the original region and solving a system of inequality equations. Second, improving the focusing evaluation function and combining this function with the dichotomy method transforms the discriminating minimum of the evaluation function into the detection of the zero-crossing position, reducing the number of searches required for the autofocusing method. The effectiveness of the proposed autofocusing method is validated through numerical simulations and experiments. The proposed method can significantly improve computational efficiency while guaranteeing the detection accuracy of the focusing plane compared with conventional methods.

Autofocusing in off-axis digital Fresnel holography using S-th power weighted neighborhood correlation coefficient

Accurately determining the focus plane of the reconstructed image is crucial for obtaining high-quality reconstructed images in the process of digital hologram reconstruction. In this paper, a focusing evaluation function based on S-th power weighted neighborhood correlation coefficient (SPWNCC) is proposed to realize automatic focusing of the reconstructed image in off-axis digital Fresnel holography. The Fresnel transform method is utilized as the off-axis digital holographic reconstruction algorithm. Both the numerical simulation and optical experiment results are given to verify the validity of the proposed autofocusing method. The obtained focusing curve can maintain good unimodality and noise immunity performance over a large search range (approximately 800 mm). The proposed SPWNCC based focusing evaluation function has a certain guiding significance on the automatic focusing of off axis digital holographic reconstructed images of long distance recorded objects.

Depth-of-field expansion method based on multidimensional structure and edge-guided correction.

Multi-focus image fusion is a method to extend the depth of field to generate fully focused images. The effective detection of image focusing pixels and the optimization of image regions are the key to it. A method based on multidimensional structure and edge-guided correction (MSEGC) is proposed. The pixel-level focusing evaluation function is redesigned to preserve image details and non-texture regions. Edge-guided decision correction is used to suppress edge artifacts. With public data and semiconductor detection images for verification, the results show that compared with other methods, the objective evaluation is improved by 22-50%, providing better vision.

Improving fast auto-focus with event polarity.

Fast and accurate auto-focus in adverse conditions remains an arduous task. The emergence of event cameras has opened up new possibilities for addressing the challenge. This paper presents a new high-speed and accurate event-based focusing algorithm. Specifically, the symmetrical relationship between the event polarities in focusing is investigated, and the event-based focus evaluation function is proposed based on the principles of the event cameras and the imaging model in the focusing process. Comprehensive experiments on the public event-based autofocus dataset (EAD) show the robustness of the model. Furthermore, precise focus with less than one depth of focus is achieved within 0.004 seconds on our self-built high-speed focusing platform. The dataset and code will be made publicly available.

Maximum Gradient Autofocus Technology of Microsporidia Images Based on Color Feature

There are many impurities in the microscopic images of extracted microsporidia samples of Bombyx mori pebrine, and Bombyx mori pebrine with elliptical symmetric shape has certain fluidity and obvious stratification. Traditional focusing methods cannot accurately locate the main regions of microsporidia images, and the focusing effect is poor. On this basis, an automatic focusing method combining the microsporidia image features and the evaluation and determination of maximum gradient direction is proposed. First, the HSV color space with stable color information is used to extract the suspected positions of microsporidia targets, so that the interference of some impurities under complex backgrounds is removed and the redundancy of image content calculation is reduced. Then, combined with the light green features of Bombyx mori pebrine, the G-component gray image of microsporidia in the RGB color space is used to extract the significant gradient region. A dynamic focus window is constructed to accurately locate the target region and reduce the influence of microsporidia flow on the focus evaluation function and the bimodal interference caused by impurities. Finally, the maximum second-order difference is obtained through the four-dimensional gradient distribution, and the focus sharpness evaluation function is formulated to adapt to the microsporidia shape and improve the sensitivity of the focus function. The experiments show that under the dynamic window of microsporidia color gradient of different samples, the sharpness ratio and the highest sensitivity factor of the focus evaluation function proposed in this paper can reach 0.06341 and 0.95, respectively. It can meet the accurate and sensitive autofocus of microscopic images of color microsporidia samples under complex backgrounds.

Selection of Optimal Focusing Evaluation Function Based on Sparse Microscopic Images

The auto focusing process is affected by the amount of image content, resulting in the focus curve not meeting the characteristics of the ideal focus curve. In this paper, aiming at the problem that the traditional autofocus cannot find the focal plane successfully in the case of microscopic sparse content, the qualitative analysis and quantitative analysis of 12 traditional focus evaluation functions are carried out. The experimental results show that the F Tenengrad function is suitable for the optimal focus evaluation function of sparse microscopic images.

Automatic Console Image Processing Aided by Improved Particle Swarm Computing Intelligent Algorithm

The search range and search position of the particle are closely related to the shrinkage-expansion factor of the particle and the value of the center of the potential well. As the number of iterations increases, its search will gradually fall near the center of the potential well, and the search range will gradually decrease. Therefore, when the center of the potential well and the search range gradually approach the global optimum, the final result of the algorithm can be guaranteed to be the global optimum. Therefore, we need to optimize the shrinkage-expansion factor and the potential well center, improve the individual development ability of the group in the later stage of the algorithm and the expansion ability of the group, so that the potential well center is easier to fall near the global optimal point. Based on the chaotic strategy, a particle swarm initialization method is proposed. Using the unique ergodicity of the chaotic system, the method can make the particles spread well in the solution space of the search problem. Based on the antecedent normal cloud model, an adaptive method for determining control parameters and the center of the potential well is proposed. Based on the consequent normal cloud model, a particle adaptive mutation method is proposed. The comprehensive application of these improvement measures to the improved QPSO effectively improves the performance of the original algorithm. In this paper, the principle and method of multioptical axis parallelism measurement are studied. Based on the hardware equipment of the parallelism measurement system based on the large-diameter off-axis parabolic mirror collimator, the measurement accuracy and automation of the system are improved through image processing technology. The degree and anti-interference ability of the system are analyzed in detail. According to the characteristics of the system, an optimized focus evaluation function and a center positioning algorithm are proposed to improve the measurement speed and accuracy of the system. Through the improvement of the system DCT focus evaluation coefficient method and the optimization of the least squares ellipse fitting process, the measurement accuracy and anti-interference ability of the system are improved, the calculation time is shortened, and the real-time performance and automation of the system are enhanced.

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

When performing digital image processing, the most critical technology that affects its use effect is the autofocus technology. With the advancement of science and the development of computer technology, autofocus technology has become more and more widely used in various fields. Autofocus technology is a key technology in robot vision and digital video systems. In order to allow digital image processing technology to better serve humans, it is necessary to further improve the focus evaluation function algorithm. This article focuses on the imaging principle of defocused images, using different evaluation functions to analyze and process the experimental images to observe the changes in image clarity. Through the introduction and analysis of the existing evaluation function, it can be known that the focus evaluation function will directly affect the quality of digital image processing. Therefore, it is best to choose unimodality, unbiasedness, low noise sensitivity, wide coverage, and a small amount of calculation. For the evaluation function, the Laplacian gradient function is an ideal choice. However, because the current digital image processing technology is not perfect enough, the focus function is still prone to multiextreme problems when the image is severely defocused and the high‐frequency components in the image are missing; the balance between image processing speed and focus accuracy also still needs to be improved. Therefore, this paper studies the autocontrol microscope focus algorithm based on digital image processing, analyzes the principle of visual image imaging, and makes some improvements to the microscope focus algorithm. Through experiments, it can be seen that the real‐time data of the original Laplace function in the edge‐obvious target is 76.9, and it reaches 77.6 after improvement. The improved algorithm can better maintain the single‐peak state during the focusing process, which improves the image processing efficiency while ensuring the measurement accuracy.

Microscope Autofocus Method Based on Analog Image Video Signal Processing

<p style="text-align: justify;"><span style="font-family: "times new roman";">The most important part of the automatic control microscope system is the auto focus technology. How to improve the accuracy and speed of the focus is the most noteworthy breakthrough point, in which the focus evaluation function is used to evaluate whether the image is correctly focused. Objective: through the evaluation of several kinds of focus evaluation functions, and according to the application of microscope focus technology in the research, we compare and select the focus method which is more easy to use image video signal for proper processing, and analyze and experiment the key technology. Methods: according to the high frequency component of image video signal, the class divergence of the difference between class samples, choosing the appropriate evaluation function and step size through the gradient value size, comparing the sharpness and edge of the focus image and the non-focus image to evaluate the quality and clarity of image video signal, selecting a high comprehensive auto focus algorithm to realize auto focus The gray difference method, Laplace operator and other evaluation functions. Results: in the end, the resolution of the image is 93.1%, and the success rate of auto focusing is 98.2%. The method based on analog image video signal processing improves the precision and speed of focusing, and it has been successfully applied to the auto focusing system of microscope.</span>

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network.

Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.

Research on multi-focus image fusion algorithm based on total variation and quad-tree decomposition

In order to get the focus region from the two or more source image more effectively and to represent the source image completely and effectively, a decomposition strategy based on the combination of total variation and quad-tree and an improved fusion method of focus region detection are proposed. The theory of total variation and quad-tree is applied to the fusion of multi-focus images. Firstly, two registered experimental images are decomposed into the optimal block decomposition graph by total variation and quad-tree decomposition, respectively. For each block, the initial focus region decision map of the source image is found by using improved Sum-Modified-Laplacian, and the final focus region decision map is obtained by consistency test and morphological processing for the initial focus region decision map. Furthermore, compared with other algorithms, the improved algorithm has more advantages in extracting the focus area, because of improved focus evaluation function and more accurate detection of the focus area. According to the fusion source image of the final focus region decision map, the results of four sets of experiments show that the fusion quality and effect are significantly improved compared with the existing algorithm.

Defocusing mechanism and focusing evaluation function of light field imaging

The light field imaging technology can realize the application of the full-focus image synthesis of the scene and the de-occlusion reconstruction of background target through the classification of the light field information of the scene. How to effectively evaluate the focus area in the image is a prerequisite for the above application. The dispersion characteristics of the defocusing point of a conventional imaging system are fundamentally different from those of the defocusing area in the refocusing image of the light field. Therefore, the evaluation criteria based on the diffusion characteristics of the defocusing point cannot be applied to the evaluation of the focus of the refocusing image of the light field. Aiming at the above problems, in this paper, starting from the principle of refocusing of light field imaging, we analyze the blurring characteristics of the defocused target image, and propose a new evaluation function of the focus of the refocusing image of the light field. Based on this, the refinement segmentation method of the focal region is studied to achieve the final focus area extraction. According to the indoor scene data set captured by the camera array of Stanford university, in this paper we use the traditional focus degree evaluation algorithm and the algorithm to evaluate the focusing degree of the foreground target potted plant in the scene and obtain the complete information about the foreground target, therefore we also study the refined segmentation algorithm. Then, in the process of refocusing the background object (CD box), the foreground light is screened out, and the reconstructed image of the specified focusing plane is obtained. Using the peak-signal-to-noise ratio and mean structural similarity index measure to evaluate the quality of the target in refocusing area, the results show that the proposed algorithm in this paper can effectively mark and separate the imaginary artifact information and ensure the high-quality focus reconstruction of the partially occluded target in the scene, which can effectively overcome the influence of the edge and texture information of the object in the scene on the defocusing area. The method presented in this work has better adaptability to the focus degree evaluation of the refocusing image of the light field.

A fast autofocus sharpness function of microvision system based on the Robert function and Gauss fitting.

For the microvision system, a new autofocus evaluation function based on the Robert function is proposed by increasing the threshold value. Compared with the traditional evaluation function, the new focus function reduces the local extreme value and increases the steepness of the focusing curve. According to the characteristics of the focusing evaluation function, the focus curve can be divided into two stages: the gentle area and the steep area. In the gentle area, there will be set a large step-length to realize the fast search. In the steep area, the data will be fitted by Gauss method, and on the basis of the fitting results, the motor of microvision system was directly driven to achieve the focal plane and this method has been improved in real-time and accuracy.

Investigation of an Autofocusing Method for Visible Aerial Cameras Based on Image Processing Techniques

In order to realize the autofocusing in aerial camera, an autofocusing system is established and its characteristics such as working principle and optical-mechanical structure and focus evaluation function are investigated. The reason for defocusing in aviation camera is analyzed and several autofocusing methods along with appropriate focus evaluation functions are introduced based on the image processing techniques. The proposed autofocusing system is designed and implemented using two CMOS detectors. The experiment results showed that the proposed method met the aviation camera focusing accuracy requirement, and a maximum focusing error of less than half of the focus depth is achieved. The system designed in this paper can find the optical imaging focal plane in real-time; as such, this novel design has great potential in practical engineering, especially aerospace applications.

Depth map sensor based on optical doped lens with multi-walled carbon nanotubes of liquid crystal.

In this paper, we present a novel design concept for determining the depth map of three-dimensional (3D) scenes based on an electrically controlled liquid crystal (LC) lens. The advantages of the proposed method are that it does not need any mechanical movements and a large amount of computations to acquire a depth map of a 3D scene in a relatively short amount of time. The tunable-focus LC lens doped with multi-walled carbon nanotubes is to become a key optical component for determining a depth map system. Sequenced two-dimensional images of slightly different perspectives are recorded in a short time, and the depth map of the 3D scene, according to a proposed depth estimation method and a focusing evaluation function, can be acquired in a simple way. This new method to acquire a depth map based on a doped LC lens maximizes the use of the proposed LC lens. The proposed system is novel in its compact, simple, and fast features, so we believe the proposed method can open a new creative dimension in image analysis and imaging systems and can also overcome the limitations of the conventional imaging mode.

An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates.

Face Detection of Video Image Sequence Based on Human Characteristics

The study uses DSP as the core and develops agile tracking and surveillance system. It includes overall design of the system, construction of the hardware and compilation of the software. The system hardware consists of DC, color camera, cradle head, SEED-VPM642 DSP development board or pan-tilt-zoom control system. The paper analyzes com- mon algorithms of motion target detection. According to the characteristics of the acquired video images and the real-time requirements of the system, based on adjacent frame difference, the paper proposes continuous three-frame image differ- ence method to detect moving objects and uses centroid algorithm as the core algorithm of human motion tracking. The paper uses the character of human complexion and applies face detection algorithm based on complexion, which can rap- idly and accurately get the cleat face information of moving people. And the study selects the sum of the absolute value of gray difference as automatic focusing evaluation function of the design. Optimizing common focusing mountain-climb searching algorithm makes the system focus rapid and reliable, which can achieve the objective of automatic detection and trace on moving body and human head location.

An efficient projection defocus algorithm based on multi-scale convolution kernel templates

The focal problems of projection include out-of-focus projection images from the projector caused by incomplete mechanical focus and screen-door effects produced by projection pixilation. To eliminate these defects and enhance the imaging quality and clarity of projectors, a novel adaptive projection defocus algorithm is proposed based on multi-scale convolution kernel templates. This algorithm applies the improved Sobel-Tenengrad focus evaluation function to calculate the sharpness degree of intensity equalization and then constructs multi-scale defocus convolution kernels to remap and render the defocus projection image. The resulting projection defocus corrected images can eliminate out-of-focus effects and improve the sharpness of uncorrected images. Experiments show that the algorithm works quickly and robustly and that it not only effectively eliminates visual artifacts and can run on a self-designed smart projection system in real time but also significantly improves the resolution and clarity of the observer’s visual perception.

A New Automatic Focusing Algorithm and its Application on Vision Measuring Machine

The way to realize the automatic focusing rapidly and efficiently is a focus in some automatic optical inspection case. The key problem is how to select a suitable focusing evaluation function and design an effective auto-focusing algorithm. In this paper, based on comparing some well-known focusing evaluation functions, a new automatic focusing algorithm was presented. It used two steps to do the search. At the first step, it searched a large region quickly just to get the approximate focusing position, the next step was to do precise search in a small area in order to get the exact focusing position. This new algorithm performed well on the vision measuring machine and could focus rapidly and exactly.

A Three-Dimensional Topography Measurement for Micro-nano Film Buckling Based on the Focusing Evaluation Function

This research focuses on observing and measuring the topography of thin film buckle in micro-nano scale, which is based on focus measure function and Shape From Focus technology, Gaussian interpolation. A PZT stepper motor which is well calibrated is used to change the distance between thin film surface and the microscope lens. Focusing-evaluation-functions elements of each image which is taken at each step are corresponded to the distance values obtained from the PZT stepper motor. Then, focusingevaluation- functions of each element at different steps can be seemed as a function of distance values. The actual distance of each element between the film and the lens, when the element is clearest, can be obtained by the Gaussian interpolation when the element focusing-evaluation-function reaches the peak value. Furthermore, this method is applied successfully to measure the full field topography of straight side buckle and telephone cord buckle.