The existence of phase error influences the imaging performance of optical synthetic aperture system and limits the resolution of the imaging system. The relationship between point spread function of binocular system and piston error is analyzed and the conclusion that the relation between point spread function and piston error is irrational with coordinates along the perpendicular bisector of baseline has been deduced and proved. Therefore the effect of position accuracy to error detection is avoided. The distribution characteristic of point spread function on baseline determines the range of piston error and by which a unique value of piston error is obtained. Finally, the piston error detection method of binocular system is extended to the common optical synthetic aperture system. Simulation experiment demonstrates that this method can conquer effect of various noises and the detection accuracy is better than 0.004 times of wavelength.
Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
Image denoising is an important method of preprocessing, it is one of the forelands in the field of Computer Graphic and Computer Vision. Astronomical target imaging are most vulnerable to atmospheric turbulence and noise interference, in order to reconstruct the high quality image of the target, we need to restore the high frequency signal of image, but noise also belongs to the high frequency signal, so there will be noise amplification in the reconstruction process. In order to avoid this phenomenon, join image denoising in the process of reconstruction is a feasible solution. This paper mainly research on the principle of four classic denoising algorithm, which are TV, BLS - GSM, NLM and BM3D, we use simulate data for image denoising to analysis the performance of the four algorithms, experiments demonstrate that the four algorithms can remove the noise, the BM3D algorithm not only have high quality of denosing, but also have the highest efficiency at the same time.
Abstract—Image matching is the core research topics of digital photogrammetry and computer vision. SIFT(Scale-Invariant
Feature Transform) algorithm is a feature matching algorithm based on local invariant features which is proposed by Lowe at
1999, SIFT features are invariant to image rotation and scaling, even partially invariant to change in 3D camera viewpoint
and illumination. They are well localized in both the spatial and frequency domains, reducing the probability of disruption by
occlusion, clutter, or noise. So the algorithm has a widely used in image matching and 3D reconstruction based on stereo
image. Traditional SIFT algorithm's implementation and optimization are generally for CPU. Due to the large numbers of
extracted features(even if only several objects can also extract large numbers of SIFT feature), high-dimensional of the feature
vector(usually a 128-dimensional SIFT feature vector), and the complexity for the SIFT algorithm, therefore the SIFT
algorithm on the CPU processing speed is slow, hard to fulfil the real-time requirements. Programmable Graphic Process
United(PGPU) is commonly used by the current computer graphics as a dedicated device for image processing. The
development experience of recent years shows that a high-performance GPU, which can be achieved 10 times single-precision
floating-point processing performanceone compared with the same time of a high-performance desktop CPU, simultaneity the
GPU's memory bandwidth is up to five times compared with the same period desktop platform. Provide the same computing
power, the GPU's cost and power consumption should be less than the CPU-based system. At the same time, due to the parallel
nature of graphics rendering and image processing, so GPU-accelerated image processing become to an efficient solution for
some algorithm which have requirements for real-time. In this paper, we realized the algorithm by OpenGL shader language
and compare to the results which realized by CPU. Experiments demonstrate that the efficiency of GPU-based SIFT algorithm
are significantly improved.