Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.
As the commercial performance of camera sensor and the imaging quality of lens improving, it has the possibility to applicate in the space target observation. Multiple cameras can further improve the detection ability of the camera with image fusion. This paper mainly studies the multiple camera image fusion problem of registration with the imaging characteristics of a commercial camera, and then put forward an applicable method of star image registration. It proved that the accuracy of registration could reach the subpixel level with experiments.
Space targets in astronomical images such as spacecraft and space debris are always in the low level of brightness and hold a small amount of pixels, which are difficult to distinguish from fixed stars. Because of the difficulties of space target information extraction, dynamic object monitoring plays an important role in the military, aerospace and other fields, track extraction of moving targets in short-exposure astronomical images holds great significance. Firstly, capture the interesting stars by region growing method in the sequence of short-exposure images and extract the barycenter of interesting star by gray weighted method. Secondly, use adaptive threshold method to remove the error matching points and register the sequence of astronomical images. Thirdly, fuse the registered images by NCST-PCNN image fusion algorithm to hold the energy of stars in the images. Fourthly, get the difference of fused star image and final star image by subtraction of brightness value in the two images, the interesting possible moving targets will be captured by energy accumulation method. Finally, the track of moving target in astronomical images will be extracted by judging the accuracy of moving targets by track association and excluding the false moving targets. The algorithm proposed in the paper can effectively extract the moving target which is added artificially from three images or four images respectively, which verifies the effectiveness of the algorithm.
The resolution of the camera and the detection sensitivity is increasing day by day to make it possible to use on observing deep space small target. In order to satisfy the commercial camera observing the stars background targets in high dynamic image fusion and image matching in high precision and rapid extraction star location of the application requirements, analyzed the influence of different noise on star positioning accuracy, preprocessing, and then puts forward the star selection method for image registration applications, finally completed the star locating and used the altitude angle and azimuth of stars in actual stars map to analyze the accuracy of extraction.
KEYWORDS: High dynamic range imaging, Image fusion, Image processing, Astronomy, Cameras, RGB color model, Mathematical modeling, Data modeling, Data fusion, Charge-coupled devices
Astronomical detection always need high dynamic range image, but there are problems such as underexposure or overexposure in astronomical images taken by commercial camera, this paper proposed the technique that combine establishing the first order difference quotient curve of each pixel with data feature positioning to calculate optimal exposure value of each pixel, which achieves high dynamic range fusion. In this paper, data feature positioning method was firstly utilized to establish mathematical model to calculate optimal exposure point in the first order difference quotient curve of each pixel in the target scene. Correlate optimal exposure point and camera response function to calculate optimal brightness value of each pixel, the fused high dynamic range image will be achieved. Finally, take a series of low dynamic range images with different exposure value by commercial camera, establish mathematical model and calculate to achieve high dynamic range fusion, which verifies the fusion technique proposed in this paper can obtain high dynamic range astronomical images effectively.
In order to solve the low speed and low accuracy in exacting star point which used in starlight star point navigation, this paper presents an algorithm to quickly extract the coordinates of the Navistar in the image. First of all, this algorithm extracts the coordinates of star point with a low accuracy, then extracting its diffuse plaque, in the final, get its exact coordinates. Which can reduce the amount of computation to improve navigation extraction rate while avoid the time-domain filtering of the star point of the outline and diffuse spots of gray value, solving low speed in the sky diffuse plaques star point image extraction. The experiments show that this algorithm can extract the star point while making dark star and background noise greatly reduced. At the same time, star point and diffuse plaque contour gray value can be consistent with the original image.
The noise of laser images is complex, which includes additive noise and multiplicative noise. Considering the features of laser images, the basic processing capacity and defects of the common algorithm, this paper introduces the fractal theory into the research of laser image denoising. The research of laser image denoising is implemented mainly through the analysis of the singularity exponent of each pixel in fractal space and the feature of multi-fractal spectrum. According to the quantitative and qualitative evaluation of the processed image, the laser image processing technique based on fractal theory not only effectively removes the complicated noise of the laser images obtained by range-gated laser active imaging system, but can also maintains the detail information when implementing the image denoising processing. For different laser images, multi-fractal denoising technique can increase SNR of the laser image at least 1~2dB compared with other denoising techniques, which basically meet the needs of the laser image denoising technique.
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