3D Surface Morphology Measurement and Auto-focusing System

When interference microscope measures the surface rough of the micromechanical device, as soon as the work distance of interference microscope and the depth of field is shortened, the interference images become slur for the measured object if there has small interference after clear focus. The auto-focusing system is introduced into the interference microscope, the system can obtain high definition interference image rapidly,and can improve the measuring velocity and measuring precision. The system is characterized by auto-focusing range of ±150 μm, auto-focusing precision of ±0.3 μm, auto-focusing time of 4～8 s.

A fast auto-focusing method of microscopic imaging based on an improved MCS algorithm

An improved"three steps"mountain-climb searching(MCS)algorithm is proposed which is applied to auto-focusing for microscopic imaging accurately and efficiently.By analyzing the performance of several evaluation functions,the variance function and the Brenner function are synthesized as a new evaluation function.In the first step,a self-adaptive step length which is much dependent on the reciprocal of the evaluation function value at the beginning position of climbing is used for approaching the halfway up the mountain roughly.Secondly,a fixed moderate step length is applied for approaching the mountaintop of the variance function as closer as possible.Finally,afine step is employed for reaching the exact mountaintop of the Brenner function.The microscope auto-focusing experiments based on the proposed algorithm for blood smear detection have been carried out comprehensively.The results show that the improved algorithm can not only guarantee the precision to get clear focal images,but also improve the auto-focusing e±ciency.

Real-time auto-focusing technique using centroid method for space camera

A real-time auto-focusing system with auto-collimation method is introduced, which is used in auto-detecting the focus of the space camera with long focus. Auto-focusing is the key technique to ensure high quality in space imaging. It can measure and compensate the defocus caused by the change of temperature and air pressure etc. in space. To solve the problem of auto-focusing with auto-collimation method of the camera whose axis is perpendicular to the ground, it is designed that two small caliber pentagonal prisms are placed in the area of aperture suitable to the camera’s relative aperture based on the theory of auto-focusing with auto-collimation, which can replace the big caliber plane reflector used in other cameras. Using the characteristic of pentagonal prism refracting light vertically, the target slit is imaged in CCD through the two-separated lens. It transforms the detecting of the axial defocusing quantity to the landscape orientation measurement of the faculae’s position in the direction of CCD pixels. The defocusing quantity is obtained by measuring the opposite position of the two faculae on the CCD. The Centroid method is adopted to measure the position of the auto-collimation faculae. The arithmetic error is analyzed especially, and the causation is given. Experiments show that this real-time auto-focusing system using centroid method is reliable and the focusing precision can reach ±0.01 mm.

Entropy function optimization for radar imaging

The convergence performance of the minimum entropy auto-focusing（MEA） algorithm for inverse synthetic aperture radar（ISAR） imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multidimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking （DCT）algorithm. Since these values are obtained according to the maximum likelihood （ML） principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

Phase-shifting digital holography in image reconstruction

A phase-shifting digital holography scheme developed to investigate internal defects in artworks is described. Phase-shifting is utilized to obtain a clear reconstructed object wave from a rough surface texture. A reverse-transform algorithm is employed to reconstruct the object wave on its original position of unknown distance or the imaging position from the object wave information on the holographic plane. To get the clearest reconstruction the exact registration of the unknown distance is determined by applying the intensity sum as the auto-focusing function, The spatial resolution of the reconstruction image is also investigated for a variety of affecting factors. Laboratory results of reconstruction images under deformation are presented.

Efficiency Analysis of the Autofocusing Algorithm Based on Orthogonal Transforms

Efficiency of the autofocusing algorithm implementations based on various orthogonal transforms is examined. The algorithm uses the variance of an image acquired by a sensor as a focus function. To compute the estimate of the variance we exploit the equivalence between that estimate and the image orthogonal expansion. Energy consumption of three implementations exploiting either of the following fast orthogonal transforms: the discrete cosine, the Walsh-Hadamard, and the Haar wavelet one, is evaluated and compared. Furthermore, it is conjectured that the computation precision can considerably be reduced if the image is heavily corrupted by the noise, and a simple problem of optimal word bit-length selection with respect to the signal variance is analyzed.

Integrated lens auto-focus system driven by a nut-type ultrosonic motor (USM)

This paper introduces an integrated optical auto-focus system driven by a nut-type ultrasonic motor (USM). The system comprises an optical lens as a rotor (M6 or M7), a polyhedral tube of copper as a stator; an image sensor, and a driver IC of the motor. The sizes of the AF (auto-focus) module are 8.5 mm×8.5 mm×5.9 mm. The piezoelectric elements are bonded on the external surface of the stator. The rotor has external screw thread that engages with the inner screw thread of the stator. When the piezoelectric elements are excited by the driver IC, a bend traveling wave in plane is generated on the stator along the circle direction, that drives the lens rotor to rotate and then to move axially. The driver IC is controlled by an image feedback of an image sensor centered on the axis of the casing, then the optical focusing is realized. The power consumption is zero at rest and is less than 0.25 W in motion; focusing precision <10 μm; speed >3 r/s(180 r/min); response <10 ms; high reliability: resistant to shock and fall off; directly driven by the driver IC without transmission mechanism; the friction force is namely the driving force and noiseless. The integrated optical auto-focus system is very useful, especially for cellular phones. The image resolution of 3―5 MP has been obtained in the module prototypes of the cellular phone.

A fast face detection architecture for auto-focus in smart-phones and digital cameras

Auto-focus is very important for capturing sharp human face centered images in digital and smart phone cameras. With the development of image sensor technology, these cameras support more and more highresolution images to be processed. Currently it is difficult to support fast auto-focus at low power consumption on high-resolution images. This work proposes an efficient architecture for an Ada Boost-based face-priority auto-focus. The architecture supports block-based integral image computation to improve the processing speed on high-resolution images; meanwhile, it is reconfigurable so that it enables the sub-window adaptive cascade classification, which greatly improves the processing speed and reduces power consumption. Experimental results show that 96% detection rate in average and 58 fps(frame per second) detection speed are achieved for the1080p(1920×1080) images. Compared with the state-of-the-art work, the detection speed is greatly improved and power consumption is largely reduced.