To satisfy the requirement of the astronomical observation, a novel timing sequence of frame transfer CCD is proposed. The multiple functions such as the adjustments of work pattern, exposure time and frame frequency are achieved. There are four work patterns: normal, standby, zero exposure and test. The adjustment of exposure time can set multiple exposure time according to the astronomical observation. The fame frequency can be adjusted when dark target is imaged and the maximum exposure time cannot satisfy the requirement. On the design of the video processing, offset correction and adjustment of multiple gains are proposed. Offset correction is used for eliminating the fixed pattern noise of CCD. Three gains pattern can improve the signal to noise ratio of astronomical observation. Finally, the images in different situations are collected and the system readout noise is calculated. The calculation results show that the designs in this paper are practicable.
Compressed sensing theory is a new sampling theory that can sample signal in a below sampling rate than the traditional Nyquist sampling theory. Compressed sensing theory that has given a revolutionary solution is a novel sampling and processing theory under the condition that the signal is sparse or compressible. This paper investigates how to improve the theory of CS and its application in imaging system. According to the properties of wavelet transform sub-bands, an improved compressed sensing algorithm based on the single layer wavelet transform was proposed. Based on the feature that the most information was preserved on the low-pass layer after the wavelet transform, the improved compressed sensing algorithm only measured the low-pass wavelet coefficients of the image but preserving the high-pass wavelet coefficients. The signal can be restricted exactly by using the appropriate reconstruction algorithms. The reconstruction algorithm is the key point that most researchers focus on and significant progress has been made. For the reconstruction, in order to improve the orthogonal matching pursuit (OMP) algorithm, increased the iteration layers make sure low-pass wavelet coefficients could be recovered by measurements exactly. Then the image could be reconstructed by using the inverse wavelet transform. Compared the original compressed sensing algorithm, simulation results demonstrated that the proposed algorithm decreased the processed data, signal processed time decreased obviously and the recovered image quality improved to some extent. The PSNR of the proposed algorithm was improved about 2 to 3 dB. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
As the focusing range of optical imaging system is generally limited, it is difficult to make all the objects of the same scene clearly shown in one image. Besides, a case usually rose that the fused image with a high entropy, however, is not satisfying for vision effect. In this paper, a new method of multi-focus image fusion based on adaptive dividing blocks using comprehensive index was proposed, in which the comprehensive index was on basis of spatial frequency and entropy. The comprehensive index is better with the higher spatial frequency and entropy. Firstly, the registered original images were divided into a series of blocks of which the sizes were proper and the same, and then the comprehensive index for each block of source images was calculated as the focus criterion function to select an optimal block for each corresponding block of the fused image. In view of the relevance between pixel and pixel in one image, the optimal blocks selected were fused with a global fusion function. Furthermore, the sum-modified-Laplacian of fused image was used as the measure function to supervise the adaptive blocking, in which the optimal block was obtained when SML of the fused image had reached a high value or the iteration had achieved the specified numbers. Finally, the optimal size of the sub-block was automatically obtained, which was used to fuse the source images. As it was shown in the experimental results, the proposed method which was simple, but more effective compared with the traditional multiscale decomposing methods such as wavelet transform, wavelet packet transform, contourlet transform and so on. At the same time, the proposed method was also superior to the method in the literature for it could remove boundary discontinuities between image blocks. Contemporarily, much more details and edges information of the source images were reserved in the fused image.
Proc. SPIE. 8759, Eighth International Symposium on Precision Engineering Measurement and Instrumentation
KEYWORDS: Signal to noise ratio, Diffraction, Optical transfer functions, Point spread functions, Stars, Image processing, Error analysis, Charge-coupled devices, Phase transfer function, Space operations
Star tracker has been widely used as a precise and reliable device for the attitude measuring of a spacecraft. The accuracy of star location will affect the accuracy of star identification and finally the accuracy of attitude measurement. This paper proposed a novel method to locate the star position with the phase transfer function (PTF). The numerical expressions are deduced with the diffraction model of the star point in 1-D and given directly in 2-D. Then calculation is performed and the accuracy is better than 2.1% pixels (SNR=20) with a 3×3 window of airy disk, which is higher than the traditional centroid method. Different sizes of the airy disk from 3 to 6 are simulated with PTF method and we find that the optimal window size is 3 to 5. Finally the Additive Gaussian Noise with SNR from 2 to 40 is introduced to evaluate the novel method and compare it with the traditional centroid method. The accuracy of the new method can reach better than 2.5% pixels and it is much robust than the traditional centroid method, which proves that the method we proposed has a good performance under the noise environment.