The panoramic annular optical lens can acquire the information of 360 degrees scene of horizontal plane and condense the image onto a CCD. In this paper a single-lens catadioptric panoramic imaging system is designed by the software ZEMAX to form a panoramic annular image on a CCD which is needed to be unwrapped for easily observation. An improved cubic spline interpolation algorithm is proposed in this paper based on the characters of the annular image for the unwrapping of the image. The new method can preserve the edge and detail of the image much better compared with the nearest interpolation algorithm and bilinear interpolation algorithm. The image already unwrapped still has distortion due to the intrinsic imaging characters of the catadioptric panoramic imaging system. Then the paper shows the evaluation for the distortion of the unwrapped image which derived from the result of image simulation using the optical system mentioned above. A new distortion method is devised when black and white fringe image is used as the input picture for the image simulation to get a distortion correction model which can be applied for the distortion correction of any other unwrapped images. Simulation results show that the panoramic annular optical system can obtain the omnidirectional information factually based on the new unwrapped algorithm and distortion correction algorithm.
Using high-speed visual equipment is an effective method to locate mobile targets. Under the circumstance of high
sensitivity(500Hz), except for the Gaussian noise, atmospheric instability has also an important impact on the image
quality. To solve the problem, a method is proposed in this paper based on image power spectrum to analyze and
evaluate the Gaussian noise, atmospheric noise meanwhile combined with wavelet denoising to remove the noise aiming
at the images acquired by DALSA's 8192*32 high-sensitivity camera. Firstly, image databases are established based on
the outdoor working conditions, including normal images, Gaussian noise images loaded with different simulated
characteristics and atmospheric noise images in different simulated frequencies. Power spectrum ratio of all the images
in the databases is calculated, and the image power spectrum critical value is determined. Then the evaluation and
classification of the image noise is got according to the databases and the threshold. wavelet denoising is introduced to
remove the noise subsequently. Finally, the comparison of power spectrum between the image untreated and treated is
made to evaluate the effect of the method above. Experimental results show that the way can evaluate and remove the
noise of image effectively for high-speed visual images.
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