本文關(guān)鍵詞： 相位恢復(fù) 強(qiáng)度傳輸方程 空間光調(diào)制 相位調(diào)制 振幅調(diào)制　出處：《安徽大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：光波場(chǎng)的性質(zhì)可以從三個(gè)方面來完全描述:振幅(亮度)、波長(zhǎng)(顏色)和相位(一個(gè)波長(zhǎng)內(nèi)相位等同于深度)。統(tǒng)計(jì)表明80%以上的信息保存在相位項(xiàng)中,由此可見,相對(duì)于強(qiáng)度等其他信息,相位信息顯得尤為重要。然而,光波場(chǎng)的振蕩頻率(約為1015Hz)遠(yuǎn)高于現(xiàn)有最高速的光探測(cè)器幀頻(108Hz),探測(cè)器只能測(cè)量出光波場(chǎng)的強(qiáng)度變化,而無法直接探測(cè)到光波場(chǎng)的相位信息。因此,需要利用測(cè)量的強(qiáng)度分布來計(jì)算相位信息,即為相位恢復(fù)問題。目前,相位恢復(fù)已經(jīng)應(yīng)用到光學(xué)測(cè)量、天文學(xué)成像、電子顯微學(xué)、自適應(yīng)光學(xué)、光學(xué)相位顯微等眾多領(lǐng)域�；趶�(qiáng)度傳輸方程的相位恢復(fù)是一種典型的非干涉確定性相位恢復(fù)方法。該方程建立了光強(qiáng)度的軸向變化量與相位之間的定量關(guān)系,只需要測(cè)量物面的強(qiáng)度分布,就可以通過求解該方程直接計(jì)算出相位信息。相較于干涉相位恢復(fù)方法,基于強(qiáng)度傳輸方程的相位恢復(fù)不需要復(fù)雜的光學(xué)系統(tǒng),對(duì)于實(shí)驗(yàn)環(huán)境要求不苛刻。另外,求解過程不需要迭代,且求解出的相位不需要解纏。然而,基于強(qiáng)度傳輸方程求解的結(jié)果存在分辨率低以及采集圖像時(shí)需要機(jī)械地移動(dòng)成像器件CCD等缺點(diǎn),易受到強(qiáng)度差分高階近似的非線性誤差和測(cè)量噪聲的影響,因而,限制了恢復(fù)結(jié)果的精度�？臻g光調(diào)制器本質(zhì)上是一個(gè)適應(yīng)性光學(xué)裝置,它能在波陣面上施加空間和時(shí)間變化調(diào)制,改變波陣面的振幅、相位和偏振。本文利用空間光調(diào)制器對(duì)光場(chǎng)調(diào)制的特性,將其應(yīng)用到相位恢復(fù)的相關(guān)計(jì)算中,提出了新的相位恢復(fù)算法,搭建了相應(yīng)的實(shí)驗(yàn)平臺(tái),獲得了具有高質(zhì)量的相位恢復(fù)結(jié)果。主要研究工作和創(chuàng)新點(diǎn)如下:(1)基于空間域相位調(diào)制的相位恢復(fù),研究了基于傾斜光照合成的相位恢復(fù)算法。將空間光調(diào)制器放置在空間域中,加載不同角度的傾斜光柵對(duì)入射光場(chǎng)進(jìn)行相位調(diào)制,并通過合成孔徑技術(shù)計(jì)算出相位,提高了恢復(fù)圖像的分辨率,獲得了具有高質(zhì)量的恢復(fù)結(jié)果。(2)基于空間域振幅調(diào)制的相位恢復(fù),提出了基于余弦光柵調(diào)制和強(qiáng)度傳輸方程的相位恢復(fù)算法。將空間光調(diào)制器放置在空間域中,加載余弦光柵對(duì)入射光場(chǎng)進(jìn)行振幅調(diào)制,利用獲取的強(qiáng)度圖像求解強(qiáng)度傳輸方程計(jì)算出相位偏導(dǎo)數(shù),進(jìn)一步地由相位偏導(dǎo)數(shù)恢復(fù)出相位信息。并通過實(shí)驗(yàn)驗(yàn)證了該算法的有效性,實(shí)驗(yàn)結(jié)果表明該算法可有效地抑制噪聲對(duì)恢復(fù)結(jié)果的影響。(3)基于頻率域振幅調(diào)制的相位恢復(fù),提出了基于正弦光柵調(diào)制的相位恢復(fù)算法。將空間光調(diào)制器放置在頻率域中,加載正弦光柵對(duì)光場(chǎng)的振幅進(jìn)行調(diào)制,通過強(qiáng)度與相位偏導(dǎo)數(shù)之間的關(guān)系計(jì)算出相位偏導(dǎo)數(shù),從而進(jìn)一步獲得相位信息。設(shè)計(jì)了光學(xué)實(shí)驗(yàn)平臺(tái),可快速采集到強(qiáng)度圖像,避免了傳統(tǒng)方法中機(jī)械地移動(dòng)成像器件造成的誤差,并利用采集的真實(shí)圖像計(jì)算出相位分布。模擬實(shí)驗(yàn)與真實(shí)實(shí)驗(yàn)結(jié)果驗(yàn)證了該算法的正確性和有效性。
[Abstract]:The properties of the optical wave field can be described in three aspects: amplitude (luminance), wavelength (color) and phase (the phase within a wavelength is equal to depth). Statistics show that more than 80% information is stored in the phase term. Compared with other information, such as intensity, the phase information is particularly important. However, the oscillation frequency of the optical wave field (about 1015Hz) is much higher than the frame rate of the most high-speed photodetector (108Hz), and the detector can only measure the intensity change of the optical wave field. But the phase information of light wave field can not be detected directly. Therefore, it is necessary to use the intensity distribution of measurement to calculate the phase information, that is, phase recovery problem. At present, phase recovery has been applied to optical measurement, astronomical imaging, electron microscopy. Adaptive optics, optical phase microscopy and so on. The phase recovery based on the intensity transfer equation is a typical non-interference deterministic phase recovery method. The equation establishes the quantitative relationship between the axial variation of optical intensity and the phase. The phase information can be directly calculated by solving the equation by measuring the intensity distribution of the object surface. Compared with the interferometric phase recovery method, the phase recovery based on the intensity transmission equation does not require complex optical systems. The requirements for the experimental environment are not harsh. In addition, the solution process does not require iteration, and the phase of the solution does not require unwrapping. However, The results based on the intensity transfer equation have the disadvantages of low resolution and the need to move the imaging device CCD mechanically when collecting images, which are easily affected by the nonlinear error of high-order approximation of intensity difference and measurement noise. The spatial light modulator is essentially an adaptive optical device that can apply spatial and temporal modulation on the wavefront to change the amplitude of the wavefront. Based on the characteristics of spatial light modulator for light field modulation, this paper applies it to the calculation of phase recovery, proposes a new phase recovery algorithm, and builds a corresponding experimental platform. The results of phase recovery with high quality are obtained. The main work and innovations are as follows: 1) Phase recovery based on spatial phase modulation. The phase recovery algorithm based on tilted light synthesis is studied. The spatial light modulator is placed in the spatial domain, and the incident light field is modulated by a tilted grating with different angles, and the phase is calculated by the synthetic aperture technique. The resolution of the restored image is improved, and the phase recovery based on amplitude modulation in spatial domain is obtained. A phase recovery algorithm based on cosine grating modulation and intensity transfer equation is proposed. The spatial light modulator is placed in the spatial domain and cosine grating is loaded to modulate the incident light field. Using the obtained intensity image to solve the intensity transfer equation, the phase partial derivative is calculated, and the phase information is further recovered from the phase partial derivative. The validity of the algorithm is verified by experiments. Experimental results show that the proposed algorithm can effectively suppress the effect of noise on the recovery results. The phase recovery algorithm based on amplitude modulation in frequency domain is proposed. A phase recovery algorithm based on sinusoidal grating modulation is proposed. The spatial light modulator is placed in the frequency domain. The amplitude of light field is modulated by loading sinusoidal grating, the phase partial derivative is calculated by the relation between intensity and phase partial derivative, and the phase information is further obtained. The error caused by mechanical moving imaging device in the traditional method is avoided, and the phase distribution is calculated by using the collected real image. The correctness and validity of the algorithm are verified by the simulation and real experiment results.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN761;TP391.41

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