Optimizing the sparse basis is an effective way to enhance single-pixel imaging performance. Compressed sensing typically employs discrete wavelet basis to map signals into the wavelet domain to achieve approximate sparsity, where wavelet coefficients resemble an exponential decay form. However, in the penalty term of cost function, large lowfrequency wavelet coefficients carry higher weights, while the weights assigned to small high-frequency coefficients are much smaller. This implies that high-frequency coefficients are easily neglected in optimization and are even mistaken as noise and removed, resulting in the loss of image details.We propose an effective method that introduces a diagonal matrix W with exponentially increasing diagonal elements to balance the weights of low-frequency and high-frequency wavelet coefficients, ensuring the weights of high-frequency coefficients are ample to prevent them from being mistakenly treated as noise and discarded.For normalized images of size 256*256 with 25% sampling, proposed method are applied to several common compressed sensing algorithms for single-pixel imaging reconstruction such as L1-minimization, LASSO, and OMP. The simulation results indicate an average improvement of 1.10dB, 1.32dB, and 2.65dB in PSNR, respectively. Even in the presence of strong Gaussian noise with σ =0.2, the method can still partly enhance reconstruction performance.This research provides a novel perspective on optimizing the sparse basis and a practical approach to improving single-pixel imaging performance.
In ground-based large aperture solar telescopes, the speckle image reconstruction technique combined with adaptive optics (AO) correction is generally used to get near diffraction-limited solar images. In order to select the high quality images from the AO-corrected high resolution solar image sequence, an automated no-reference image quality assessment (IQA) is needed. According to the noise characteristics of solar AO images, an IQA metric based on an image power spectrum and human visual system is developed. By the incorporation of noise masking and shifting the spatial frequency range of summation, our IQA metric could select sharper images with less noise than previous works based on the image power spectrum, even if there is image scaling. Compared with existing general-purpose IQA metrics and previous metrics specifically designed for solar IQA, experimental results verify that the proposed metric gains better performance whether on robustness to blur and noise, or on selecting high quality frames containing solar granulations, sunspots, or both of them.
KEYWORDS: Denoising, Signal to noise ratio, Data modeling, Chemical species, Parallel computing, Sun, Large telescopes, Associative arrays, Computer programming languages, Computer programming
The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform.
The resolution of the astronomical object observed by the earth-based telescope is limited due to the atmospheric turbulence. Speckle image reconstruction method provides access to detect small-scale solar features near the diffraction limit of the telescope. This paper describes the implementation of the reconstruction of images obtained by the 1-m new vacuum solar telescope at Full-Shine solar observatory. Speckle masking method is used to reconstruct the Fourier phases for its better dynamic range and resolution capabilities. Except of the phase reconstruction process, several problems encounter in the solar image reconstruction are discussed. The details of the implement including the flat-field, image segmentation, Fried parameter estimation and noise filter estimating are described particularly. It is demonstrated that the speckle image reconstruction is effective to restore the wide field of view images. The qualities of the restorations are evaluated by the contrast ratio. When the Fried parameter is 10cm, the contrast ratio of the sunspot and granulation can be improved from 0.3916 to 0.6845 and from 0.0248 to 0.0756 respectively.
Based on the demands of high sensitivity, precision and frame rate of tip/tilt tracking sensors in acquisition, tracking and pointing (ATP) systems for satellite-ground optical communications, this paper proposes to employ the multiple-anode photo-multiplier tubes (MAPMTs) in tip/tilt tracking sensors. Meanwhile, an array-type photon-counting system was designed to meet the requirements of the tip/tilt tracking sensors. The experiment results show that the tip/tilt tracking sensors based on MAPMTs can achieve photon sensitivity and high frame rate as well as low noise.
The pyramid wavefront sensor is an innovative device with the special characteristics of variable gain and adjustable
sampling in real time to enable an optimum match of the system performance, which make it an attractive option for next
generation adaptive optics system compared with the Shack-Hartmann. At present most of the pyramid wavefront sensor
are used with modulation based on oscillating optical component in order to give a linear measurement of the local tilt,
but the PWFS without modulation would greatly simplify the optical and mechanical design of the adaptive optics
system and also give highest sensitivity as expected to be achieved. In this paper we describe the optical setup of our
adaptive optics system with nonmudulated pyramid wavefront sensor. In this system, the pyramid wavefront sensor with
8×8 sub-apertures in the pupil diameter has been designed, and the deformable mirror with 61 actuators based on the
liquid-crystal spatial light modulator is used to introduce aberrations into the system, as well as to correct them
afterwards. The closed-loop correction results of single order Zernike aberrations and the Kolmogorov turbulence phase
screen are given to show that the PWFS without modulation can work as expected for closed-loop system.
The adaptive optics can only partially compensate the image blurred by atmospheric turbulence due to the observing
condition and hardware restriction. A post-processing method based on frame selection and multi-frames blind
deconvolution to improve images partially corrected by adaptive optics is proposed. The appropriate frames which are
suitable for blind deconvolution from the recorded AO close-loop frames series are selected by the frame selection
technique and then do the multi-frame blind deconvolution. There is no priori knowledge except for the positive
constraint in blind deconvolution. It is benefit for the use of multi-frame images to improve the stability and
convergence of the blind deconvolution algorithm. The method had been applied in the image restoration of celestial
bodies which were observed by 1.2m telescope equipped with 61-element adaptive optical system at Yunnan
Observatory. The results show that the method can effectively improve the images partially corrected by adaptive
optics.
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