本文關(guān)鍵詞： 自適應(yīng)光學(xué) 大氣湍流 波前復(fù)原 小波域的Curvelet變換 極大似然估計(jì)　出處：《長春工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：大氣湍流效應(yīng)引起的大氣折射率隨機(jī)起伏,會(huì)導(dǎo)致光波波前扭曲,嚴(yán)重影響光學(xué)系統(tǒng)成像性能,降低觀測目標(biāo)圖像分辨率。自適應(yīng)光學(xué)技術(shù)是克服大氣湍流最有效的辦法之一,但由于自適應(yīng)光學(xué)系統(tǒng)自身性能及噪聲等因素的影響,其只能對波前誤差進(jìn)行部分校正,所觀測到的目標(biāo)圖像分辨率依然與系統(tǒng)衍射極限相差很遠(yuǎn),因此,對湍流退化圖像進(jìn)行后續(xù)復(fù)原處理是至關(guān)重要的。為了提高湍流圖像分辨率,本文重點(diǎn)研究了湍流退化圖像去噪及復(fù)原算法:首先,研究自適應(yīng)光學(xué)系統(tǒng)。介紹自適應(yīng)光學(xué)系統(tǒng)結(jié)構(gòu)、工作及成像原理,分析了云南天文臺1.2米望遠(yuǎn)鏡的61單元自適應(yīng)光學(xué)系統(tǒng)參數(shù)及性能,并設(shè)計(jì)了雙校正器自適應(yīng)光學(xué)系統(tǒng)光路圖。通過分析后已知,自適應(yīng)光學(xué)系統(tǒng)對波前畸變的校正是不充分的,需對觀測到的湍流圖像進(jìn)行后續(xù)復(fù)原處理。其次,研究波前復(fù)原技術(shù)及湍流波前相位屏模擬。研究Zernike多項(xiàng)式法波前復(fù)原技術(shù),利用Zernike多項(xiàng)式法模擬了不同項(xiàng)數(shù)下大氣湍流隨機(jī)相位屏的湍流分布情況。從模擬結(jié)果可知,Zernike多項(xiàng)式項(xiàng)數(shù)較低時(shí),湍流屏上缺乏高頻成分,而低頻成分突出,相位相對平穩(wěn);隨著Zernike多項(xiàng)式項(xiàng)數(shù)的增加,其湍流屏上的湍流分布逐漸變緩,相位平穩(wěn)度下降。再次,研究基于小波域的Curvelet變換法的湍流退化圖像去噪算法。將多尺度幾何變換的思想引入到湍流圖像去噪中,首先介紹小波域的Curvelet變化原理,然后根據(jù)貝葉斯準(zhǔn)則,改進(jìn)了閾值的自適應(yīng)選取方法,提取二維小波變換高頻系數(shù)并對其進(jìn)行基于Wrapping的Curvelet變換,最終實(shí)現(xiàn)了小波域的Curvelet變換法對湍流退化圖像的去噪處理。實(shí)驗(yàn)結(jié)果表明,該算法處理后的湍流圖像視覺效果清晰、相比于原始圖像PSNR明顯提高,MSE明顯降低。最后,研究改進(jìn)極大似然估計(jì)法的大氣湍流圖像復(fù)原算法。首先對湍流圖像進(jìn)行了去噪處理,然后利用Zernike多項(xiàng)式對湍流退化圖像進(jìn)行波前復(fù)原,根據(jù)波前信息建立了點(diǎn)擴(kuò)散函數(shù)初值估計(jì)模型,接著結(jié)合圖像先驗(yàn)信息,引入目標(biāo)邊緣保持約束及點(diǎn)擴(kuò)散函數(shù)約束改進(jìn)了ML算法,在迭代求解目標(biāo)圖像估計(jì)和點(diǎn)擴(kuò)散函數(shù)F估計(jì)過程中,對點(diǎn)擴(kuò)散函數(shù)進(jìn)行了頻域帶寬約束,最終利用該算法對模擬湍流圖像及實(shí)際觀測湍流圖像進(jìn)行了復(fù)原處理。實(shí)驗(yàn)結(jié)果表明,該算法可以有效提高湍流復(fù)原圖像分辨率,復(fù)原后圖像的灰度平均梯度有明顯提高,圖像能量更加集中。
[Abstract]:The random fluctuation of atmospheric refractive index caused by atmospheric turbulence effect will lead to the distortion of optical wavefront and seriously affect the imaging performance of optical system. Adaptive optics is one of the most effective methods to overcome atmospheric turbulence, but due to the influence of the performance of adaptive optical system and noise and other factors. It can only partially correct the wavefront error and the resolution of the observed image is still far from the diffraction limit of the system. In order to improve the resolution of turbulent images, the de-noising and restoration algorithms of turbulent degraded images are studied in this paper. The structure, operation and imaging principle of adaptive optics system are introduced. The parameters and performance of the 61 unit adaptive optics system of the 1.2-meter telescope of Yunnan Observatory are analyzed. The optical circuit diagram of adaptive optical system with double corrector is designed. It is known by analysis that the correction of wavefront distortion by adaptive optics system is not sufficient, and it is necessary to carry out follow-up restoration of the observed turbulence image. Secondly. The wave front restoration technique and turbulent wavefront phase screen simulation are studied. The Zernike polynomial wave front restoration technique is studied. The Zernike polynomial method is used to simulate the turbulent distribution of atmospheric turbulent random phase screen under different term numbers. From the simulation results, it can be seen that the number of Zernike polynomials is low. There is no high frequency component on the turbulence screen, but the low frequency component is prominent and the phase is relatively stable. With the increase of Zernike polynomial terms, the turbulence distribution on the turbulent screen becomes slower and the phase stability decreases. The denoising algorithm of turbulent degraded image based on Curvelet transform in wavelet domain is studied, and the idea of multi-scale geometric transformation is introduced into the denoising of turbulent image. Firstly, the principle of Curvelet variation in wavelet domain is introduced, and then the adaptive threshold selection method is improved according to Bayesian criterion. The high frequency coefficients of two-dimensional wavelet transform are extracted and the Curvelet transform based on Wrapping is carried out. Finally, the Curvelet transform in wavelet domain is implemented to remove the noise of the turbulent degraded image. The experimental results show that the visual effect of the proposed algorithm is clear. Compared with the original image, the PSNR is significantly improved. Finally, the improved maximum likelihood estimation (MLE) algorithm for atmospheric turbulence image restoration is studied. Firstly, the turbulent image is de-noised. Then the Zernike polynomial is used to restore the wavefront of the turbulent degenerate image, and the initial value estimation model of the point diffusion function is established according to the wavefront information, and then the prior information of the image is combined. The ML algorithm is improved by introducing the edge preserving constraint and the point diffusion function constraint. In the iterative solution of the target image estimation and the point diffusion function F estimation, the point diffusion function is constrained in frequency domain bandwidth. Finally, the proposed algorithm is used to reconstruct the simulated and observed turbulent images. The experimental results show that the proposed algorithm can effectively improve the resolution of turbulent restored images. After restoration, the average grayscale gradient of the reconstructed image is obviously improved, and the image energy is more concentrated.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;O439

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