Focusing Window Research Articles

Defocus compensation scheme for femtosecond laser direct writing of large-area non-periodic micro-structures

The optical path error, the position-independent geometric error, and the form and position tolerance of the substrate will lead to position deviations of the laser focus resulting in the inconsistency of the large area micro-structure and limiting the processing range of the femtosecond laser fabrication platform. To realize continuous defocus compensation along the processing trajectory, an autofocus scheme consisting of an optimized depth from focus (DFF) method based on grid division and the bilinear interpolation algorithm is proposed in this paper. The optimized DFF method includes the Laplace-DWT evaluation function combining the advantages of spatial domain and frequency domain sharpness evaluation function, an adaptive focusing window selection method, and an adaptive step length three-step hill-climbing search method. The optimized DFF method was used to obtain the best focus position of each grid point. Compared with the traditional DFF method, the optimized algorithm has higher focusing precision and avoids the disadvantage of time-consuming focusing. Then, to compensate for interpolating points of the processing trajectory, the bilinear interpolation algorithm was introduced to achieve online focusing during two-photon lithography processing. Finally, the feasibility and effectiveness of the auto-focusing scheme were validated by experiments. Through defocus compensation, the sharpness evaluation value of the focus image on the processing trajectory improved by 90%, which indicated the successful focus of the laser beam on the workpiece surface. Moreover, a high-precision and large-area encoded micro-polarizer array with consistent structure were fabricated successfully. This paper provides a simple way to identify and compensate the defocus errors online with high accuracy and efficiency. The proposed method can improve the machining accuracy and quality of the femtosecond laser direct writing.

Automatic Focusing Method of Microscopes Based on Image Processing

Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

Applications of improved artificial fish swarm algorithm in microscopy autofocus

The selection of the focusing window is the key procedure in achieving precise automatic focus of the microscope. For the traditional focus window selection method, the limitation is that the target object cannot be accurately positioned. This paper proposes an improved artificial fish focusing window method. The method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position. Under the premise of ensuring accuracy, the elimination behavior is introduced to improve the convergence speed of the algorithm in the later period. Furthermore, according to the characteristics of the bulletin board in the algorithm, the interference area is identified with the trend comparison method, and the influence of the non-target area is effectively excluded. Experiment results show that the focusing window obtained by this algorithm can be well-suited for the target object, greatly improve the accuracy of autofocus, and make the efficiency improvement 1.65 times than the traditional method.

眼底照相机自动调焦算法

An auto focusing method based on auto focusing window selection was presented according to the imaging principle and characteristics of fundus images.The preliminary focusing window is extracted through the focusing window selection algorithm based on local entropy with improved threshold.The final window is located through multi-directional stepping algorithm.The image sharpness evaluation function combined with the characteristics of local contrast sharpness evaluation function and gradient vector square function was presented according to low contrast fundus images.Experimental results show that the presented auto focusing window selection algorithm ensure the stability and accuracy for the focusing window,and the image sharpness evaluation function has better sensitiveness for low contrast fundus images.

Constant-Momentum Acceleration Time-of-Flight Mass Spectrometry with Energy Focusing

Fundamental aspects of constant-momentum acceleration time-of-flight mass spectrometry (CMA-TOFMS) are explored as a means to improve mass resolution. By accelerating all ions to the same momentum rather than to the same energy, the effects of the initial ion spatial and energy distributions upon the total ion flight time are decoupled. This decoupling permits the initial spatial distribution of ions in the acceleration region to be optimized independently, and energy focus, including ion turn-around-time error, to be accomplished with a linear-field reflectron. Constant-momentum acceleration also linearly disperses ions across time according to mass-to-charge (m/z) ratio, instead of the quadratic relationship between flight time and m/z found in conventional TOFMS. Here, CMA-TOFMS is shown to achieve simultaneous spatial and energy focusing over a selected portion of the mass spectrum. An orthogonal-acceleration time-of-flight system outfitted with a reduced-pressure DC glow discharge (GD) ionization source is used to demonstrate CMA-TOFMS with atomic ions. The influence of experimental parameters such as the amplitude and width of the time-dependent CMA pulse on mass resolution is investigated, and a useful CMA-TOFMS focusing window of 2 to 18Da is found for GD-CMA-TOFMS.

A novel training based auto-focus for mobile-phone cameras

In this paper, we propose a fast and accurate training based auto-focus (AF) method for mobile-phone cameras. Given a set of training data, the proposed method collects feature vectors consisting of focus value increment ratio. The representative feature vectors corresponding to every possible best in-focus lens position (BILP) are stored in the database. In the proposed training based AF method, the BILP is obtained by comparing the input feature vector with the representative feature vectors in the database. To further enhance the accuracy of the proposed AF method, we also introduce a new focusing window that is effective in detecting the target object. The experimental results show that the proposed method can accurately estimate the BILP faster than the existing methods.

Adaptive focusing window for seismic angle migration

Kirchhoff migration is based on a continuous integral ranging from minus infinity to plus infinity. The necessary discretization and truncation of this integral introduces noise in the migrated image. The attenuation of this noise has been studied by many authors who propose different strategies. The main idea is to limit the migration operator around the specular point. This means that the specular point must be known before migration and that a criterion exists to determine the size of the migration operator. We propose an original approach to estimate the size of the focusing window, knowing the geologic dip. The approach benefits from the use of prestack depth migration in angle domain, which is recognized as the most artifact-free Kirchhoff-type migration. The main advantages of the method are ease of implementation in an existing angle-migration code (two or three dimensions), user friendliness, ability to take into account multiorientation of the local geology as in faulted regions, and flexibility with respect to the quality of the estimated geologic dip field. Common-image gathers resulting from the method are free from migration noise and can be postprocessed in an easier way. We validate the approach and its possibilities on synthetic data examples with different levels of complexity.

Protein purification by counteracting chromatographic electrophoresis ? The focusing window

Counteracting Chromatographic Electrophoresis (CACE) is a novel purification technique that focuses the target protein into a thin band at the interface separating two gel media which differ sharply in internal porosity. This article validates a criterion that predicts protein focusing by CACE, using the colored proteins-ferritin, hemoglobin and myoglobin. Fair comparison is shown between the theory and the experiment. Data from the past literature which were reported to exhibit nonconformity have also been shown to agree fairly well with the present model. Variations in protein focusing conditions can be partly explained by protein-ion binding.