本文關(guān)鍵詞： 紅外成像 弱小目標(biāo)檢測(cè) 各向異性擴(kuò)散濾波 奇異值分解 多尺度幾何分析　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：紅外探測(cè)技術(shù)利用目標(biāo)和背景之間的紅外輻射差異來(lái)進(jìn)行目標(biāo)探測(cè),具有全天時(shí)工作、隱蔽性強(qiáng)和抗干擾性好等優(yōu)點(diǎn),因而得到了廣泛的應(yīng)用。然而,遠(yuǎn)距離下目標(biāo)成像面積非常小,同時(shí)成像目標(biāo)的輻射強(qiáng)度也相對(duì)較弱,特別是當(dāng)目標(biāo)處于復(fù)雜的背景環(huán)境中,目標(biāo)甚至可能被復(fù)雜的背景所淹沒,信雜比更低,使得紅外弱小目標(biāo)檢測(cè)的難度極大地提高。因此,對(duì)復(fù)雜背景下的紅外弱小目標(biāo)檢測(cè)技術(shù)進(jìn)行深入研究,有著極為重要的理論意義和實(shí)際的工程應(yīng)用價(jià)值。本文首先對(duì)紅外圖像中的目標(biāo)和背景的輻射特性進(jìn)行了分析,并運(yùn)用多尺度幾何分析的方法研究了目標(biāo)與背景在不同尺度和不同方向的表現(xiàn)形式,為后面提出新的目標(biāo)檢測(cè)算法提供理論支持。其次,介紹了偏微分方程理論在圖像與信號(hào)處理中的應(yīng)用,由于其中的各向異性擴(kuò)散方程具有顯著的各向異性,可以將其進(jìn)行離散化處理,運(yùn)用到濾波中,本文對(duì)擴(kuò)散系數(shù)進(jìn)行修正,并通過與原始圖像作差得到一種改進(jìn)的各向異性擴(kuò)散差分濾波。然后,將非下采樣Contourlet變換與奇異值分解引入到紅外弱小目標(biāo)檢測(cè)中,提出了一種基于非下采樣Contourlet變換與奇異值分解的紅外弱小目標(biāo)檢測(cè)算法。該算法首先將原始圖像進(jìn)行奇異值分解,選取適當(dāng)數(shù)目的奇異值重構(gòu)背景信息;再通過與原始圖像的差分運(yùn)算來(lái)抑制背景;然后進(jìn)行非下采樣Contourlet變換,在變換域中再次利用奇異值分解來(lái)保存目標(biāo)信息、抑制背景和濾除噪聲;最終經(jīng)過非下采樣Contourlet反變換即可實(shí)現(xiàn)復(fù)雜背景下的紅外弱小目標(biāo)檢測(cè)。采用真實(shí)的紅外圖像進(jìn)行了仿真實(shí)驗(yàn),結(jié)果表明了該算法的有效性。最后,研究了Surfacelet變換,提出了一種基于Surfacelet變換與各向異性擴(kuò)散方程的紅外弱小目標(biāo)檢測(cè)算法。該算法首先利用Surfacelet變換對(duì)原始圖像進(jìn)行分解,得到一系列的高頻方向子帶和低頻子帶;然后分別采用各向異性擴(kuò)散差分濾波和局部去均值濾波對(duì)高頻方向子帶和低頻子帶進(jìn)行處理,以此來(lái)突顯目標(biāo)和抑制背景,最終實(shí)現(xiàn)復(fù)雜背景下的紅外弱小目標(biāo)檢測(cè)。采用真實(shí)的紅外圖像進(jìn)行了仿真實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明該算法可以有效地實(shí)現(xiàn)紅外弱小目標(biāo)的檢測(cè),同時(shí)具有非常不錯(cuò)的魯棒性。
[Abstract]:The infrared detection technology uses the infrared radiation difference between the target and the background to carry on the target detection, has the advantage of all-day operation, strong concealment and good anti-jamming, so it has been widely used. The imaging area of the target is very small at a long distance, and the radiation intensity of the imaging target is relatively weak, especially when the target is in the complex background environment, the target may even be submerged by the complex background, and the signal-to-clutter ratio is lower. The difficulty of infrared dim target detection is greatly improved. Therefore, the infrared dim target detection technology under complex background is studied in depth. It has very important theoretical significance and practical engineering application value. Firstly, the radiation characteristics of target and background in infrared image are analyzed in this paper. And the multi-scale geometric analysis method is used to study the representation of target and background in different scales and different directions, which provides theoretical support for the proposed new target detection algorithm. Secondly. The application of partial differential equation theory in image and signal processing is introduced. Because the anisotropic diffusion equation has obvious anisotropy, it can be discretized and applied to filtering. In this paper, the diffusion coefficient is modified, and an improved anisotropic diffusion differential filter is obtained by differentiating with the original image. Non-downsampling Contourlet transform and singular value decomposition (SVD) are introduced into infrared dim target detection. This paper presents an infrared small and weak target detection algorithm based on non-downsampling Contourlet transform and singular value decomposition, which firstly decomposes the original image by singular value decomposition. Select a proper number of singular values to reconstruct the background information; Then the background is suppressed by the difference operation with the original image. Then the non-downsampling Contourlet transform is carried out, and the singular value decomposition is used again in the transform domain to save the target information, suppress the background and filter the noise. Finally, the infrared dim target detection under complex background can be realized by non-downsampling Contourlet inverse transform. The real infrared image is used for simulation experiment. The results show that the algorithm is effective. Finally, the Surfacelet transform is studied. An infrared small and weak target detection algorithm based on Surfacelet transform and anisotropic diffusion equation is proposed. Firstly, the Surfacelet transform is used to decompose the original image. A series of high frequency subbands and low frequency subbands are obtained. Then the anisotropic diffusion difference filter and the local de-mean filter are used to process the high-frequency directional subband and the low-frequency subband respectively to highlight the target and suppress the background. Finally, the infrared small and weak target detection under the complex background is realized. The real infrared image is used to carry out the simulation experiment, and the experimental results show that the algorithm can effectively realize the infrared small and weak target detection. At the same time, it has very good robustness.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41;TN215

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