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.
A new method of straightness measurement is developed in this paper. It makes interference beam be the reference axis in order to measure the straightness of rail or other objects under test. This method can reduce the measurement error brought by the thermal distortion of laser source and environmental factors in a certain extent, and the accuracy of straightness measurement is advanced.
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