The application of a light-field camera based on an overlapped square aperture micro-lens array (MLA) is reported. The location and direction of the incident light rays are recorded and reconstructed with a large depth of field by this imaging system. The optimized square MLA for light-field imaging, which increases the fill factor and utilization efficiency of the image sensor, is proposed. The array is fabricated by ultra-precision machining and micro-injection molding, which is economical and ensures that the array remains in high accuracy and consistency. Finally, a light-field imaging system is built with lens groups, an MLA, and an adjustable square stop. A comparative study of the field in-depth and super-resolution image is conducted. The experimental results show that the system significantly increases the range of the depth of the field. The reconstructed image is also enhanced with higher resolution and clarity.
A novel curved compound eye imaging system is put forward in this paper. Non-uniform hexagonal lens array is
arranged on the inner surface of a plano-concave substrate. Based on the geometrical optics, the parameters of each
microlens are set according to the position of the lens, and even orders of aspheric lens are used to correct some primary aberrations. Optical parameters of this configuration are entered into numerical ray-tracing simulations (ZEMAX). The result shows that the new curved compound eye can enlarge the field of view (FOV) approximately 50% compared to the lateral compound eye, and the FOV can be up to 150°. The principles and functions of all parts of system are described in detail. At last, the feasibility of ultra-precision machining is studied in this paper.
Taking the advantages of FPGA's low cost and compact structure, an FPGA-based heterogeneous image fusion platform
is established in this study. Altera's Cyclone IV series FPGA is adopted as the core processor of the platform, and the
visible light CCD camera and infrared thermal imager are used as the image-capturing device in order to obtain dualchannel
heterogeneous video images. Tailor-made image fusion algorithms such as gray-scale weighted averaging,
maximum selection and minimum selection methods are analyzed and compared. VHDL language and the synchronous
design method are utilized to perform a reliable RTL-level description. Altera's Quartus II 9.0 software is applied to
simulate and implement the algorithm modules. The contrast experiments of various fusion algorithms show that,
preferably image quality of the heterogeneous image fusion can be obtained on top of the proposed system. The applied
range of the different fusion algorithms is also discussed.
A new level measuring system for oil tankers based on machine vision is designed for realizing close-cabin operating
and remote monitoring. The system adopts ARM9 S3C2240 microchip as the central processing unit. With a high-precision
macro-focusing CCD sensor and an image capturing module, the system can acquire the level ruler images and
process them with a series of algorithms. Pre-processing procedures to the captured ruler images, including binarizing
and denoising methods, are implemented to improve the image quality. A grey level projecting program is used to
extract the rectangular area containing digit characters and segment the digits into individual parts. Following judgment
strategies are executed to separate the exact digit of the characters. Each character is scanned with vertical and horizontal
lines at various positions. Pixel change point numbers are counted to distinguish different digit characters to proceed the
recognition procedure. The scale in the viewing field can be accurately localized, so that the automatic recognition result
is obtained. The experimental results for different oil levels indicate that the measuring accuracy of this system can
achieve ±0.1 mm and the automatic reading time is less than 0.5 s, which shows the characteristics of high-precision and
high-speed.
A new image fusion method based on Contourlet transform and an improved pulse coupled neural network (PCNN) is
introduced in this paper. The input infrared and visible images are processed with Contourlet decomposition which has
multi-scale and multi-directional characteristics. The PCNN algorithm deriving from the neurophysiology is optimized in
order to be compatible with the image fusion strategy. Owning to the global coupling and pulse synchronization
characteristic of PCNN, this new fusion strategy utilizes the global features of source images and has several advantages
in comparison with the traditional methods based on the single pixel or regional features. Multiple criteria and statistical
indicators regarding different aspects of image quality are presented for objective and quantitative evaluation of the fused
images to understand the performance of image fusion algorithms. Experimental result shows that the new method can
improve the quality of image fusion and can achieve an ideal fusing effect. The method would find its application in the
aspects of optical imaging, target detection and safety monitoring, etc.
Wavelet threshold denoising is widely used in the denoising of the infrared image for its simplicity and effectiveness in
application. However, there has been a growing awareness to the observation that wavelets may not be the best choice for
describing infrared images. This observation is due to the fact that wavelets are blind to the smoothness along the edges
commonly found in images. A denoising method of infrared image based on Contourlet transform is presented in this
paper. In selecting the hard threshold function to process the coefficients in the Contourlet domain, we could thereby
obtain the denoised infrared image of superior quality via inverse transforming. The result of the experiment indicates
that compared with the traditional algorithms of the wavelet, this method can preserve the detail and the texture of the
infrared image more effectively, and has better image effect and the SNR value.
Aiming at the limitations of the existing measuring technology, the paper presents a novel method for inclination angle
measurement of inertial platform based on LD-PSD. The proposed scheme adopts autocollimation principle, using a laser
diode(LD) as the light source and a two-dimensional position sensitive detector(PSD) for laser spot sensing. The light
from LD is first converted to parallel light, and then projected onto a reflector on the inertial platform. The returned light
falls on PSD at last. When the inclination angle of inertial platform moves, the laser spot on PSD changes
correspondingly, providing 2-D inclination angle information of inertial platform. According to its working principle, a
mathematical model of inclination angle measurement is established. High accuracy and long stable working time are the
key indexes to implement the measurement system. Considering several main factors including the uniformity and size
of laser beam, laser beam excursion, temperature and laser interference etc, which influence the precision and stability,
improving methods are put forward. The measurement system, consisting of optical structure, PSD signal processing and
inclination angle calculation, is introduced in detail. Finally, calibration and experiment are carried out to verify its
performance. The result of the experiment shows that the resolution of the measurement system reaches 0.05" with a
working rang of ±600" and the indication error is less than ±0.5" within 24 hours. The measurement system can exactly
and reliably measure the inclination angle of inertial platform and monitor the drift of inclination angle for a long time.
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