發(fā)布時(shí)間：2018-05-12 07:36

  本文選題：圖像融合 + 非下采樣Contourlet變換　； 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：隨著傳感器成像質(zhì)量的不斷提高,如何利用紅外與可見光圖像融合技術(shù)增強(qiáng)圖像質(zhì)量與清晰度逐漸成為圖像處理與機(jī)器視覺研究領(lǐng)域的熱點(diǎn)問題�？梢姽鈧鞲衅鞒上穹先搜塾^察,含有豐富的細(xì)節(jié)信息,但是容易受到天氣影響,不能全天候工作。紅外傳感器成像穩(wěn)定,能夠很好地顯示隱藏的目標(biāo),受照明條件和惡劣天氣的影響較小,但是所得紅外圖像對(duì)比度較低,目標(biāo)細(xì)節(jié)的反映能力比較差。因此將紅外與可見光圖像融合,可彌補(bǔ)兩者的不足,發(fā)揮各自的優(yōu)勢(shì),使得融合圖像同時(shí)具有紅外與可見光圖像的優(yōu)點(diǎn)。近年來,紅外與可見光圖像融合技術(shù)已經(jīng)取得較大進(jìn)展,但融合圖像失真、紋理細(xì)節(jié)信息缺失、目標(biāo)顯著性等問題仍是圖像融合研究領(lǐng)域尚未完全解決的重點(diǎn)與難點(diǎn)。針對(duì)上述問題,本文提出基于RPCA分解模型的紅外與可見光圖像融合方法,主要工作如下:1.針對(duì)自然場(chǎng)景下的紅外與可見光圖像精確配準(zhǔn)問題,本文首先對(duì)紅外與可將光圖像融合的過程、層次、常用方法以及融合規(guī)則進(jìn)行概述;然后對(duì)圖像預(yù)處理過程進(jìn)行介紹;最后對(duì)圖像預(yù)處理過程中的圖像配準(zhǔn)技術(shù)進(jìn)行闡述,并通過實(shí)驗(yàn)與分析驗(yàn)證本文所選用紅外與可見光圖像配準(zhǔn)方法的精度與穩(wěn)定性。為后續(xù)紅外與可見光圖像融合的預(yù)處理提供可靠基礎(chǔ)。2.針對(duì)紅外與可見光圖像的特征描述問題,本文在魯棒主成分分析的基礎(chǔ)上,通過對(duì)紅外與可見光圖像進(jìn)行RPCA分解,并對(duì)分解后所得稀疏矩陣圖與低秩矩陣圖所包含的源圖像特征信息進(jìn)行分析,提出紅外與可見光圖像的RPCA分解模型。3.針對(duì)傳統(tǒng)基于非下采樣Contourlet變換的圖像融合方法易出現(xiàn)融合圖像失真、紋理細(xì)節(jié)信息缺失的問題,本文在RPCA分解模型的基礎(chǔ)上,提出基于RPCA分解模型的NSCT域紅外與可見光圖像融合方法。首先對(duì)紅外與可見光圖像進(jìn)行RPCA分解,得到相應(yīng)的稀疏矩陣;然后利用NSCT變換將紅外與可見光圖像進(jìn)行分解,得到源圖像的低頻子帶和高頻方向子帶;對(duì)于低頻子帶,采用基于稀疏矩陣的融合規(guī)則進(jìn)行融合;對(duì)于高頻方向子帶,最高層方向子帶采用基于稀疏矩陣的絕對(duì)值取大法進(jìn)行融合,其它層則采用基于PCNN的方法進(jìn)行融合;最后對(duì)融合后的低頻子帶與高頻方向子帶進(jìn)行NSCT逆變換,從而獲得最終的融合圖像。4.分別對(duì)標(biāo)準(zhǔn)圖庫與真實(shí)場(chǎng)景圖像集使用Contourlet變換融合方法、雙重NSCT融合方法、NSCT-小波-PCNN融合方法以及本文方法進(jìn)行測(cè)試實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,相對(duì)于其它幾種融合方法,本文方法不僅能夠突顯紅外圖像中的目標(biāo)人物信息,而且將可見光圖像中的紋理以及細(xì)節(jié)信息表現(xiàn)的更為細(xì)膩,同時(shí)能夠減少融合圖像失真。并對(duì)融合后圖像使用圖像互信息、平均梯度、標(biāo)準(zhǔn)差、峰值性噪比、結(jié)構(gòu)相似度指數(shù)這五項(xiàng)客觀評(píng)價(jià)指標(biāo)進(jìn)行評(píng)價(jià),評(píng)價(jià)結(jié)果表明本文方法取得較為優(yōu)異的表現(xiàn)。
[Abstract]:With the continuous improvement of sensor imaging quality, how to improve image quality and clarity using infrared and visible image fusion technology has gradually become a hot issue in the field of image processing and machine vision. Visible light sensor imaging is consistent with human eye observation and contains rich details, but it is vulnerable to weather and can not work all the time. The infrared sensor imaging is stable and can display the hidden target very well. It is less affected by the illumination condition and the bad weather, but the contrast of the infrared image is low, and the target detail ability is poor. Therefore, the fusion of infrared and visible images can make up for the shortcomings of the two images, give play to their respective advantages, so that the fusion images have the advantages of infrared and visible images at the same time. In recent years, infrared and visible image fusion technology has made great progress, but the fusion image distortion, texture details missing, target significance and other problems are still not fully resolved in the field of image fusion. Aiming at the above problems, this paper proposes an infrared and visible image fusion method based on RPCA decomposition model. The main work is as follows: 1. Aiming at the problem of accurate registration of infrared and visible images in natural scene, this paper firstly summarizes the process, level, common methods and fusion rules of infrared and visible images, and then introduces the process of image preprocessing. Finally, the image registration technology in the process of image preprocessing is described, and the accuracy and stability of the infrared and visible image registration methods selected in this paper are verified by experiments and analysis. To provide a reliable basis for the subsequent infrared and visible image fusion preprocessing. 2. Based on the robust principal component analysis (PCA), the infrared and visible images are decomposed by RPCA, aiming at the feature description of infrared and visible images. The characteristic information of the source image contained in the sparse matrix graph and the low-rank matrix graph is analyzed, and the RPCA decomposition model of infrared and visible image is proposed. In view of the problem that the traditional image fusion method based on non-downsampling Contourlet transform is prone to the distortion of fusion image and the lack of texture detail information, this paper is based on the RPCA decomposition model. An infrared and visible image fusion method in NSCT domain based on RPCA decomposition model is proposed. First, the infrared and visible images are decomposed by RPCA, and the corresponding sparse matrix is obtained. Then the infrared and visible images are decomposed by NSCT transform to obtain the low frequency subbands and the high frequency directional subbands of the source image. The fusion rules based on sparse matrix are adopted. For the high frequency directional subband, the maximum direction subband is fused by the absolute value based on sparse matrix, and the other layers are fused by PCNN method. Finally, the NSCT inverse transformation of the low frequency subband and the high frequency direction subband is carried out to obtain the final fusion image. 4. The Contourlet transform fusion method, the dual NSCT fusion method and the NSCT- wavelet-PCNN fusion method are used to test the standard image library and the real scene image set. The experimental results show that compared with other fusion methods, this method can not only highlight the target information in infrared images, but also make the texture and detail information in visible images more delicate. At the same time, it can reduce the distortion of fusion image. The five objective evaluation indexes such as mutual information, average gradient, standard deviation, peak noise ratio and structural similarity index are evaluated. The evaluation results show that the proposed method achieves excellent performance.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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