本文選題：紅外圖像　切入點(diǎn)：周期條紋噪聲　出處：《深圳大學(xué)》2017年碩士論文

【摘要】：隨著海洋資源的大力開發(fā),海洋經(jīng)濟(jì)迅速發(fā)展,海洋權(quán)益爭(zhēng)端日漸頻繁,海上交通活動(dòng)日益繁忙,迫切需要對(duì)本國領(lǐng)海進(jìn)行實(shí)時(shí)感知監(jiān)控。紅外熱成像技術(shù)以其作用距離遠(yuǎn)、隱蔽性強(qiáng)、穿透能力強(qiáng)、抗干擾性好、目標(biāo)識(shí)別能力強(qiáng)、全天候工作等特點(diǎn)在海上目標(biāo)檢測(cè)、識(shí)別方面到了應(yīng)用,成為其中一種重要的海上目標(biāo)探測(cè)手段。本文主要研究了海上紅外圖像的周期條紋噪聲去除方法,海上紅外圖像序列的海水區(qū)域提取方法,以及海上紅外圖像序列的海面目標(biāo)檢測(cè)方法。論文的具體工作和貢獻(xiàn)可以概括為如下四個(gè)方面:1.針對(duì)紅外圖像中存在周期條紋噪聲的去除問題,本文分析了周期條紋噪聲的空間特性和頻域特性,并推導(dǎo)了周期條紋噪聲在幅度譜上的梳狀沖激譜特性。在此基礎(chǔ)上,提出了一種子圖尺寸的最優(yōu)估計(jì)方法,在濾除噪聲之前,先將原始圖像以最優(yōu)尺寸裁剪為兩個(gè)子圖,使子圖中的梳狀譜具有理想沖激的性質(zhì),從而更有利于后期的噪聲檢測(cè)和噪聲頻率分量的濾除。此外,本文還給出了兩種濾波器的設(shè)計(jì)方法,自適應(yīng)梳狀陷波器的設(shè)計(jì)方法和局部線性插值濾波器的設(shè)計(jì)方法。實(shí)驗(yàn)結(jié)果表明,本文提出的方法能夠更有效地去除在紅外圖像中的周期條紋噪聲,從而提高了紅外圖像的質(zhì)量,更有利于后續(xù)的海上目標(biāo)檢測(cè)。2.針對(duì)海上紅外圖像序列的海水區(qū)域提取問題,本文分別沿水平方向和垂直方向?qū)兒Ｋ尘霸诳臻g域和頻率域的幾種特征進(jìn)行統(tǒng)計(jì)分析,發(fā)現(xiàn)海水背景在各方向上的灰度標(biāo)準(zhǔn)差以及各方向上的頻率分量幅度均值具有較穩(wěn)定的特性,具有一定的聚集性。在此基礎(chǔ)上,提出了一種基于空-頻特征高斯建模的紅外圖像海水區(qū)域提取方法。該算法首先沿不同方向提取特征空間的主成分分量,然后利用Parzen窗函數(shù)對(duì)特征向量進(jìn)行概率密度估計(jì),并通過單高斯函數(shù)擬合概率密度函數(shù),最后得到各方向的海水背景模型,并通過建立的海水背景模型提取出純海水背景區(qū)域。3.針對(duì)海上紅外圖像序列的海上目標(biāo)檢測(cè)問題,本文提出了一種基于海水背景的雙向半傅里葉域海面紅外目標(biāo)檢測(cè)算法,該算法首先根據(jù)水平方向和垂直方向的海水背景模型構(gòu)造雙向半傅里葉域海水背景抑制濾波器,并分別對(duì)原始圖像進(jìn)行濾波處理,然后對(duì)濾波后的結(jié)果進(jìn)行融合增強(qiáng),最后采用基于Otsu的閾值分割方法進(jìn)行目標(biāo)提取。實(shí)驗(yàn)結(jié)果表明,本文提出的目標(biāo)檢測(cè)算法針對(duì)本實(shí)驗(yàn)室所處理的海上紅外視頻進(jìn)行目標(biāo)檢測(cè)時(shí),具有誤檢區(qū)域少,目標(biāo)區(qū)域更完整,對(duì)非均勻光照噪聲有一定的魯棒性等優(yōu)點(diǎn)。4.在上述研究工作的基礎(chǔ)上,本文設(shè)計(jì)并實(shí)現(xiàn)了兩個(gè)海上紅外序列圖像處理軟件:紅外序列圖像預(yù)處理軟件,海面紅外序列圖像的目標(biāo)檢測(cè)算法驗(yàn)證軟件。
[Abstract]:With the rapid development of marine resources and the rapid development of marine economy, marine rights disputes become more and more frequent, and maritime traffic activities become more and more busy. It is urgent to monitor the territorial waters in real time. Infrared thermal imaging technology is far away from its role. Features such as strong concealment, strong penetration, good anti-jamming, strong target recognition ability, all-weather work, etc., have been applied to the detection and recognition of targets at sea. In this paper, we mainly study the method of removing periodic fringe noise from infrared image and the method of extracting sea water region of infrared image sequence. The specific work and contribution of this paper can be summarized as follows: 1. Aiming at the problem of periodic fringe noise removal in infrared image, In this paper, the spatial and frequency-domain characteristics of periodic fringe noise are analyzed, and the comb impulse spectrum characteristics of periodic fringe noise in amplitude spectrum are derived. On this basis, an optimal method for estimating the size of subgraph is proposed. First, the original image is cut into two subgraphs with the optimal size, so that the comb spectrum in the subgraph has the property of ideal impulse, which is more advantageous to the later noise detection and the filtering of the noise frequency component. In addition, In this paper, two kinds of filter design methods, adaptive comb notch filter and local linear interpolation filter, are presented. The experimental results show that, The method proposed in this paper can remove the periodic fringe noise in infrared image more effectively, improve the quality of infrared image, and be more favorable to the subsequent marine target detection .2. aiming at the sea water region extraction problem of the infrared image sequence at sea, the method proposed in this paper can improve the quality of the infrared image and improve the quality of the infrared image. In this paper, several characteristics of pure seawater background in spatial domain and frequency domain are statistically analyzed along the horizontal and vertical directions, respectively. It is found that the standard deviation of sea water background in each direction and the mean value of frequency component amplitude in each direction have relatively stable characteristics and have a certain degree of aggregation. A sea water region extraction method for infrared image based on space-frequency feature Gao Si is proposed. Firstly, the principal components of the feature space are extracted in different directions, and then the probability density of the feature vector is estimated by using the Parzen window function. By fitting the probability density function with single Gao Si function, the sea background model in each direction is obtained, and the pure seawater background region is extracted by the established sea water background model. The sea target detection problem based on the infrared image sequence is presented. In this paper, a bidirectional half-Fourier sea surface infrared target detection algorithm based on sea water background is proposed. Firstly, a bidirectional half-Fourier sea water background suppression filter is constructed according to horizontal and vertical sea background models. The original image is processed by filtering, then the result is fused and enhanced. Finally, the threshold segmentation method based on Otsu is used to extract the target. The experimental results show that, The target detection algorithm proposed in this paper has less error detection area and more complete target area, when the target detection algorithm is used to detect the target in the marine infrared video processed in our laboratory. On the basis of the above research work, we design and implement two marine infrared sequence image processing software: infrared sequence image preprocessing software. Verification software for target detection algorithm of infrared images of sea surface.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 朱粵媛;;我國海洋資源的合理開發(fā)與保護(hù)[J];江西農(nóng)業(yè);2016年05期

2 李海;劉新龍;周盟;劉維建;;基于修正自適應(yīng)匹配濾波器的機(jī)動(dòng)目標(biāo)檢測(cè)方法[J];雷達(dá)學(xué)報(bào);2015年05期

3 王荔霞;謝維信;裴繼紅;;多高斯模型特征空間覆蓋學(xué)習(xí)的海洋航攝圖像分割[J];電子學(xué)報(bào);2014年10期

4 楊荷英;王首勇;鄭作虎;;基于分?jǐn)?shù)低階歸一化匹配濾波的目標(biāo)檢測(cè)方法[J];空軍預(yù)警學(xué)院學(xué)報(bào);2014年05期

5 王荔霞;謝維信;李利勇;裴繼紅;;一種遙感多光譜圖像去云霧方法[J];深圳大學(xué)學(xué)報(bào)(理工版);2013年06期

6 唐沖;惠輝輝;;基于Matlab的高斯曲線擬合求解[J];計(jì)算機(jī)與數(shù)字工程;2013年08期

7 張路;張志勇;肖山竹;盧煥章;;基于多向背景預(yù)測(cè)的紅外弱小目標(biāo)檢測(cè)[J];信號(hào)處理;2010年11期

8 邱道尹;張文靜;顧波;劉新宇;;幀差法在運(yùn)動(dòng)目標(biāo)實(shí)時(shí)跟蹤中的應(yīng)用[J];華北水利水電學(xué)院學(xué)報(bào);2009年03期

9 卜凡亮;王蓉;金華;李麗華;;基于光流的運(yùn)動(dòng)目標(biāo)檢測(cè)與跟蹤[J];中國人民公安大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年02期

10 遲健男;張朝暉;王東署;王志良;;反對(duì)稱雙正交小波在紅外圖像小目標(biāo)檢測(cè)中的應(yīng)用[J];宇航學(xué)報(bào);2007年05期

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