本文關(guān)鍵詞：無人機(jī)遙感模糊圖像恢復(fù)技術(shù)研究　出處：《中國科學(xué)院長(zhǎng)春光學(xué)精密機(jī)械與物理研究所》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 無人機(jī)遙感圖像 像移模糊 航空相機(jī)異常值 大氣退化 點(diǎn)擴(kuò)散函數(shù)估計(jì) 正則化先驗(yàn) 圖像盲恢復(fù)

【摘要】：無人機(jī)遙感成像可以經(jīng)濟(jì)、快速、安全的獲取地面信息,所以在資源勘探、環(huán)境監(jiān)測(cè)、戰(zhàn)場(chǎng)偵察等領(lǐng)域具有很高的應(yīng)用價(jià)值。由于無人機(jī)航拍成像時(shí)受到惡劣天氣、發(fā)動(dòng)機(jī)振動(dòng)、自身傾斜晃動(dòng)、相機(jī)相對(duì)運(yùn)動(dòng)和大氣擾動(dòng)的影響,所捕捉到的圖像具有噪聲復(fù)雜、模糊、對(duì)比度低、細(xì)節(jié)紋理不清等特點(diǎn),使圖像質(zhì)量大大下降。為獲取清晰圖像信息,現(xiàn)今大多數(shù)圖像恢復(fù)算法都是建立在模糊圖像點(diǎn)擴(kuò)散函數(shù)已知的前提下,如逆濾波、維納濾波等,但現(xiàn)實(shí)中的成像條件卻很復(fù)雜。航空相機(jī)自身的不規(guī)則震動(dòng)、與所拍目標(biāo)間的相對(duì)運(yùn)動(dòng)、大氣湍流等都是未知的,無法精確得知模糊核函數(shù),因此對(duì)盲復(fù)原算法的研究,有著現(xiàn)實(shí)和理論的雙重需求。論文針對(duì)運(yùn)動(dòng)模糊、相機(jī)噪聲和異常值干擾模糊核估計(jì)、大氣傳輸模糊產(chǎn)生的退化圖像復(fù)原問題,分別建立優(yōu)化函數(shù)模型并設(shè)計(jì)相應(yīng)的復(fù)原方法,以去除圖像退化現(xiàn)象、提高圖像質(zhì)量與保留圖像細(xì)節(jié)信息等因素為主要切入點(diǎn)研究無人機(jī)遙感模糊圖像復(fù)原的新方法,其主要內(nèi)容如下:針對(duì)無人機(jī)航拍時(shí)的運(yùn)動(dòng)模糊問題,提出了一種基于L0稀疏先驗(yàn)的無人機(jī)遙感圖像復(fù)原算法。首先,通過對(duì)遙感圖像特性進(jìn)行分析,得出了運(yùn)動(dòng)模糊圖像的梯度分布要比清晰圖像稠密并且暗通道的稀疏性也相對(duì)較小這一固有屬性,建立了新的L0稀疏正則化復(fù)原模型。接著,針對(duì)L0范數(shù)的高度非凸性和暗通道稀疏優(yōu)化過程中涉及到的非線性最小化問題,利用查表法構(gòu)建了一種近似線性映射矩陣,并用半二次分解法對(duì)L0最小化問題進(jìn)行求解。最后,采用快速傅里葉變換在頻域中對(duì)模糊核及清晰圖像進(jìn)行交替迭代運(yùn)算,得出復(fù)原圖像。實(shí)驗(yàn)結(jié)果表明,運(yùn)動(dòng)模糊圖像得到了有效恢復(fù),各項(xiàng)圖像質(zhì)量客觀評(píng)價(jià)指標(biāo)均有顯著提升,并可以有效抑制圖像邊緣處的振鈴效應(yīng),完整保留清晰細(xì)節(jié)信息的同時(shí)顯著提高了運(yùn)算速度。由于航空相機(jī)異常值及非高斯噪聲的存在,嚴(yán)重影響了模糊核的正確估計(jì),使航拍圖像恢復(fù)效果不佳、細(xì)節(jié)丟失嚴(yán)重、人工痕跡明顯。為此,提出了一種基于消除相機(jī)異常值的飽和模糊圖像盲復(fù)原算法。首先,根據(jù)飽和圖像灰度特性建立L1正則化模型,引入超拉普拉斯先驗(yàn)提取圖像顯著邊緣。接著,針對(duì)S型函數(shù)無法完全濾除邊緣中的飽和像素,提出一種模糊核鏡像輔助函數(shù),通過設(shè)定閾值可以有效消除異常值。最后,分析異常值對(duì)模糊核估計(jì)的影響,建立基于異常值感知的盲反卷積模型,采用迭代加權(quán)最小二乘法運(yùn)算得到恢復(fù)圖像避免了迭代求解中的二次型問題。實(shí)驗(yàn)結(jié)果表明,該算法可以極大地降低航空相機(jī)異常值的影響,正確估計(jì)模糊核函數(shù),優(yōu)于傳統(tǒng)的圖像盲復(fù)原算法。最后針對(duì)無人機(jī)遙感航拍圖像在獲取過程中受到大氣擾動(dòng)影響產(chǎn)生的大氣模糊降質(zhì)問題,提出了一種基于多次散射APSF估計(jì)的大氣退化圖像恢復(fù)算法。該方法通過分析大氣對(duì)光線散射和吸收的物理特性,構(gòu)建大氣傳輸點(diǎn)擴(kuò)散函數(shù)估計(jì)模型,并設(shè)計(jì)與該模型相匹配的新算法,旨在去除無人機(jī)遙感退化圖像的大氣擾動(dòng)模糊,完成該類降質(zhì)圖像的復(fù)原。經(jīng)實(shí)驗(yàn)仿真,本文方法與其他的傳統(tǒng)算法比較,圖像恢復(fù)質(zhì)量更加優(yōu)秀,并對(duì)噪聲干擾具有一定的魯棒性。
[Abstract]:UAV remote sensing imaging can be fast, safe and economic, to obtain the information of ground, so in resource exploration, environmental monitoring, and has high application value field of Battlefield Reconnaissance UAV aerial imaging. Due to bad weather, the vibration of the engine, its tilt camera shake, relative motion and atmospheric disturbance, image capture the noise is complex, fuzzy, low contrast and texture details are not clear, so the image quality is greatly reduced. In order to obtain clear image information, most of today's image restoration algorithm is based on the premise of the known fuzzy image point spread function, such as inverse filtering, Wiener filtering, but the reality has the imaging condition very complicated. Its irregular aerial camera shake, and shoot relative motion between targets and atmospheric turbulence are unknown, not to know precisely the fuzzy kernel function, so the blind restoration algorithm The study has the dual needs of reality and theory. The thesis focuses on the motion blur, camera noise and outliers interference blur kernel estimation, atmospheric transmission blur degraded image restoration problems, establish optimization function model and design the corresponding restoration method, to remove image degradation, improve the quality of the image and preserve the detail information of the image such factors as the main starting point of the research on new method of UAV remote sensing fuzzy image restoration, its main contents are as follows: according to the motion of the UAV aerial fuzzy problem, put forward a kind of UAV remote sensing image restoration algorithm based on sparse prior L0. First of all, based on the characteristics of remote sensing image analysis, the gradient of motion blurred image the distribution of dense and dark images than the sparsity of the channel is smaller than the intrinsic property, established L0 sparse novel regularized restoration model. Then, According to the nonlinear minimization problem highly non convex L0 norm sparse optimization and dark channel involved in the process, using the look-up table method to construct an approximate linear mapping matrix, and solves the L0 minimization problem with quadratic decomposition method. Finally, in the frequency domain of fuzzy kernel and clear images are computed by the alternate iteration fast Fourier transform of the restored image. The experimental results show that the motion blurred image has been effectively restored, the objective image quality evaluation index has significantly improved, and can effectively suppress the ringing effect near the edge of the image should be intact, clear details and significantly improves the speed of operation. Because of aerial camera outliers and non Gauss noise the existence of serious impact on the correct estimation of fuzzy kernel, the aerial image restoration effect is poor, serious loss of details, artifacts. Therefore, put forward A saturated fuzzy image blind restoration algorithm to eliminate outliers based on camera. First of all, based on the L1 regularization model of gray characteristics of saturated images, extracting image edge was the introduction of Chau Laplace prior. Then, according to the S function cannot be completely saturated pixel edge filtering, proposes a fuzzy kernel image auxiliary function, by setting the threshold can be to effectively eliminate outliers. Finally, analysis of the abnormal value of fuzzy kernel estimation, establish the abnormal value of blind deconvolution based on perception model, using iterative weighted least squares method to calculate the restored image to avoid two problems in the iterative solution. The experimental results show that this algorithm can greatly reduce the influence of aerial camera outliers. The correct estimation of fuzzy kernel function, blind image restoration algorithm is better than the traditional. Finally, the UAV remote sensing aerial image to the atmosphere in the acquisition process The quality of the fuzzy drop perturbation of the atmosphere, put forward a kind of multiple scattering APSF estimation of atmospheric degradation image restoration algorithm based on this method. Through the analysis of atmospheric light scattering and absorption properties, construction of atmospheric transmission point spread function estimation model, and designs a new algorithm to match the model, in order to remove the UAV remote sensing image degradation atmospheric disturbance fuzzy, the complete degraded image restoration. The experimental simulation, compared with other traditional algorithms this method, image restoration with excellent quality, and has a certain robustness to noise.

【學(xué)位授予單位】：中國科學(xué)院長(zhǎng)春光學(xué)精密機(jī)械與物理研究所
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

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本文編號(hào)：1406666