NSCT域的多尺度景象匹配方法研究
發(fā)布時間：2019-06-16 13:26
【摘要】：景象匹配是一種依靠傳感器、圖像匹配等先進(jìn)技術(shù),對飛行器進(jìn)行精確定位的輔助導(dǎo)航技術(shù)。景象匹配指的是將一個圖像區(qū)域從同一場景的的其他設(shè)備得到的區(qū)域中定位所在位置或找到它們之間變換參數(shù)的一種重要的圖像分析和處理技術(shù)。其圖像匹配技術(shù)在導(dǎo)航制導(dǎo)的末段使用,增加匹配精度,加強制導(dǎo)系統(tǒng)的自主性。目前,景象匹配技術(shù)在遙感圖像處理、機(jī)器視覺、導(dǎo)彈制導(dǎo)、醫(yī)學(xué)等領(lǐng)域有很多的應(yīng)用。隨著國家對航空和航天技術(shù)投入,景象匹配的性能要求匹配精度逐步提高,實時性逐漸加強;同時導(dǎo)航系統(tǒng)還應(yīng)滿足飛行器高機(jī)動性、全天時工作等更高的性能要求。因此高匹配精度、實時抗干擾的匹配技術(shù)是近年來研究的重點。本文主要研究了多尺度分析的圖像匹配算法。利用非下采樣輪廓波變換(Non-subsampled Contourlet Transform,NSCT)作為多分辨率分析的工具,針對紅外和可見光圖像的成像特點,對異源圖像的景象匹配問題進(jìn)行了研究,并提出相應(yīng)算法。1.在研究了傳統(tǒng)的降噪增強算法的基礎(chǔ)上,針對紅外圖像成像質(zhì)量差等問題,本章提出了一種NSCT域和空域聯(lián)合的紅外圖像降噪和增強方法。該方法首先將算法空間變換到NSCT域,對包含噪聲的圖像系數(shù)按照3?準(zhǔn)則進(jìn)行處理,實現(xiàn)NSCT域紅外圖像降噪。然后在空域建立的歸一化非完全貝塔灰度變換模型,實現(xiàn)低分辨率下低頻圖像增強。該方法能夠完整地保留圖像輪廓特征,達(dá)到增強圖像質(zhì)量的效果。2.在研究了多尺度圖像分析理論的基礎(chǔ)上,針對以可見光圖像作匹配基準(zhǔn)圖、紅外圖像作實時圖的問題,基于多尺度分析的景象匹配算法,提出多尺度NSCT域Krawtchouk不變矩特征的景象匹配算法。從特征空間、搜索空間和搜索策略三個方面對匹配算法進(jìn)行改進(jìn),將不變矩引入特征空間利用其良好的不變性提取不變矩特征參與匹配,將特征的搜索空間帶入NSCT域進(jìn)一步壓縮搜索空間,并利用改進(jìn)的遺傳算法提高搜索速度。實驗表明本算法不僅具有更高的精度和速度,而且魯棒性好。3.利用NSCT系數(shù)的稀疏性、多尺度性和各向異性,SIFT特征在圖像描述上良好的的穩(wěn)定性,針對景象匹配改進(jìn)SIFT特征的提取,提出一種結(jié)合NSCT與改進(jìn)的SIFT的景象匹配算法。該算法在NSCT域下提取改進(jìn)的SIFT描述符,從搜索空間和特征空間兩方面增強算法的魯棒性,實驗表明該算法能抵抗一定的噪聲干擾和幾何畸變。本文研究內(nèi)容對于解決景象匹配中高精度,高可靠性的需求,提供了一種嶄新的思路,具有一定的實用價值,可應(yīng)用于可見光圖像作為匹配基準(zhǔn)圖,實時圖為紅外圖像的景象匹配問題。
[Abstract]:Scene matching is an auxiliary navigation technology which relies on advanced technologies such as sensors and image matching to accurately locate aircraft. Scene matching refers to an important image analysis and processing technique in which an image region is located from an area obtained by other devices in the same scene or where the parameters are transformed between them. The image matching technology is used in the last stage of navigation guidance to increase the matching accuracy and strengthen the autonomy of the guidance system. At present, scene matching technology has many applications in remote sensing image processing, machine vision, missile guidance, medicine and other fields. With the national investment in aviation and aerospace technology, the performance of scene matching requires that the matching accuracy is gradually improved and the real-time performance is gradually strengthened. At the same time, the navigation system should also meet the higher performance requirements of aircraft, such as high maneuverability, all-day work and so on. Therefore, the matching technology of high matching accuracy and real-time anti-interference is the focus of research in recent years. In this paper, the image matching algorithm of multi-scale analysis is studied. Using non-downsampled profile transform (Non-subsampled Contourlet Transform,NSCT) as a tool of multi-resolution analysis, the scene matching problem of heterogeneous images is studied according to the imaging characteristics of infrared and visible images, and the corresponding algorithm is proposed. On the basis of studying the traditional noise reduction and enhancement algorithm, aiming at the poor imaging quality of infrared image, a joint infrared image denoising and enhancement method in NSCT domain and spatial domain is proposed in this chapter. Firstly, the algorithm space is transformed into NSCT domain, and the image coefficients containing noise are 3? The criterion is processed to realize infrared image noise reduction in NSCT domain. Then the normalized incomplete beta gray transformation model is established in the spatial domain to realize the low frequency image enhancement at low resolution. This method can completely preserve the image outline features and achieve the effect of enhancing image quality. 2. On the basis of studying the theory of multi-scale image analysis, aiming at the problem of using visible image as matching reference map and infrared image as real-time image, based on the scene matching algorithm of multi-scale analysis, a scene matching algorithm based on multi-scale Krawtchouk invariant moment feature in multi-scale NSCT domain is proposed. The matching algorithm is improved from three aspects: feature space, search space and search strategy. The invariant moment is introduced into the feature space to extract the invariant moment feature to participate in the matching, the feature search space is brought into the NSCT domain to further compress the search space, and the improved genetic algorithm is used to improve the search speed. The experimental results show that the algorithm not only has higher accuracy and speed, but also has good robustness. Based on the sparsity, multi-scale and anisotropy of NSCT coefficients and the good stability of SIFT features in image description, a scene matching algorithm combining NSCT with improved SIFT is proposed to improve the extraction of SIFT features for scene matching. The improved SIFT descriptors are extracted in the NSCT domain to enhance the robustness of the algorithm from two aspects of search space and feature space. Experiments show that the algorithm can resist certain noise interference and geometric distortion. The research content of this paper provides a new idea to solve the demand of high precision and high reliability in scene matching, and has certain practical value. It can be applied to the scene matching problem of visible image as matching reference map and real-time image as infrared image.
【學(xué)位授予單位】：河南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2500596
[Abstract]:Scene matching is an auxiliary navigation technology which relies on advanced technologies such as sensors and image matching to accurately locate aircraft. Scene matching refers to an important image analysis and processing technique in which an image region is located from an area obtained by other devices in the same scene or where the parameters are transformed between them. The image matching technology is used in the last stage of navigation guidance to increase the matching accuracy and strengthen the autonomy of the guidance system. At present, scene matching technology has many applications in remote sensing image processing, machine vision, missile guidance, medicine and other fields. With the national investment in aviation and aerospace technology, the performance of scene matching requires that the matching accuracy is gradually improved and the real-time performance is gradually strengthened. At the same time, the navigation system should also meet the higher performance requirements of aircraft, such as high maneuverability, all-day work and so on. Therefore, the matching technology of high matching accuracy and real-time anti-interference is the focus of research in recent years. In this paper, the image matching algorithm of multi-scale analysis is studied. Using non-downsampled profile transform (Non-subsampled Contourlet Transform,NSCT) as a tool of multi-resolution analysis, the scene matching problem of heterogeneous images is studied according to the imaging characteristics of infrared and visible images, and the corresponding algorithm is proposed. On the basis of studying the traditional noise reduction and enhancement algorithm, aiming at the poor imaging quality of infrared image, a joint infrared image denoising and enhancement method in NSCT domain and spatial domain is proposed in this chapter. Firstly, the algorithm space is transformed into NSCT domain, and the image coefficients containing noise are 3? The criterion is processed to realize infrared image noise reduction in NSCT domain. Then the normalized incomplete beta gray transformation model is established in the spatial domain to realize the low frequency image enhancement at low resolution. This method can completely preserve the image outline features and achieve the effect of enhancing image quality. 2. On the basis of studying the theory of multi-scale image analysis, aiming at the problem of using visible image as matching reference map and infrared image as real-time image, based on the scene matching algorithm of multi-scale analysis, a scene matching algorithm based on multi-scale Krawtchouk invariant moment feature in multi-scale NSCT domain is proposed. The matching algorithm is improved from three aspects: feature space, search space and search strategy. The invariant moment is introduced into the feature space to extract the invariant moment feature to participate in the matching, the feature search space is brought into the NSCT domain to further compress the search space, and the improved genetic algorithm is used to improve the search speed. The experimental results show that the algorithm not only has higher accuracy and speed, but also has good robustness. Based on the sparsity, multi-scale and anisotropy of NSCT coefficients and the good stability of SIFT features in image description, a scene matching algorithm combining NSCT with improved SIFT is proposed to improve the extraction of SIFT features for scene matching. The improved SIFT descriptors are extracted in the NSCT domain to enhance the robustness of the algorithm from two aspects of search space and feature space. Experiments show that the algorithm can resist certain noise interference and geometric distortion. The research content of this paper provides a new idea to solve the demand of high precision and high reliability in scene matching, and has certain practical value. It can be applied to the scene matching problem of visible image as matching reference map and real-time image as infrared image.
【學(xué)位授予單位】：河南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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