本文選題：圖像增強(qiáng) + 圖像匹配��； 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：圖像增強(qiáng)和圖像匹配是圖像處理和計(jì)算機(jī)視覺領(lǐng)域的兩個(gè)基礎(chǔ)性的重要研究內(nèi)容。傳統(tǒng)的圖像增強(qiáng)和圖像匹配方法大多數(shù)是基于整數(shù)階微積分,對(duì)圖像中存在弱導(dǎo)數(shù)性質(zhì)的弱邊緣和弱紋理效果不理想。分?jǐn)?shù)階微分具有在增強(qiáng)圖像的同時(shí)可以更好地保留圖像中的弱邊緣和弱紋理細(xì)節(jié)信息的優(yōu)良特性,但已有的基于分?jǐn)?shù)階微分的圖像增強(qiáng)方法,需要通過人工尋找最佳階次,缺乏微分階次自適應(yīng)性;在圖像匹配中,將SIFT(Scale Invariant Feature Transform)匹配算法與分?jǐn)?shù)階微分相結(jié)合,在模糊圖像和弱紋理圖像中能提取到更多的特征點(diǎn),從而提高了匹配的精度,但是最佳微分階次的選擇仍然需要人工調(diào)整,費(fèi)時(shí)費(fèi)力。因此,本文針對(duì)這兩個(gè)問題展開研究,具體工作如下:1.通過分析分?jǐn)?shù)階微分對(duì)信號(hào)的作用,構(gòu)造了在分?jǐn)?shù)階微分圖像增強(qiáng)中的微分階次自適應(yīng)模型,該模型以反正切函數(shù)為原型,以圖像的梯度信息、局部信息熵、亮度和對(duì)比度為自變量,建立了分?jǐn)?shù)階微分階次與圖像局部信息之間的關(guān)系,從而可以根據(jù)圖像的局部特征信息自動(dòng)計(jì)算圖像中各個(gè)像素點(diǎn)的最佳階次,并將該模型應(yīng)用到分?jǐn)?shù)階微分Tiansi算子的圖像增強(qiáng)中。為了驗(yàn)證本文模型的有效性,選用標(biāo)準(zhǔn)圖像庫中的多幅紋理圖像進(jìn)行實(shí)驗(yàn),對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行了定性和定量分析,并與二階微分Laplacian算子,Tiansi算子進(jìn)行比較。理論分析和實(shí)驗(yàn)結(jié)果均可表明本文建立模型的有效性,對(duì)灰度圖像可以得到持續(xù)變化的增強(qiáng)效果,接近于最佳分?jǐn)?shù)階微分階次的增強(qiáng)實(shí)驗(yàn)效果,符合人類在視覺上的感受。2.提出了一種自適應(yīng)分?jǐn)?shù)階的SIFT算法,用于圖像匹配。算法在Riemann-Liouvill(R-L)分?jǐn)?shù)階微分的基礎(chǔ)上,設(shè)計(jì)了一種新的分?jǐn)?shù)階微分掩膜,并將其融入到SIFT算法中,提取到更多精確有效的關(guān)鍵點(diǎn),從而提高了SIFT算法的匹配精度;然后根據(jù)圖像的局部特征,構(gòu)造了分?jǐn)?shù)階微分階次自適應(yīng)數(shù)學(xué)模型。該模型以反正切函數(shù)為原型,以圖像的梯度信息、局部信息熵為自變量,建立了分?jǐn)?shù)階微分階次與圖像局部特征信息之間的關(guān)系,從而可以根據(jù)圖像的局部特征信息自動(dòng)計(jì)算圖像中各個(gè)像素點(diǎn)的最佳階次;為了驗(yàn)證本文算法和模型的有效性,選用標(biāo)準(zhǔn)庫中的圖像和真實(shí)圖像進(jìn)行實(shí)驗(yàn),與原始SIFT算法和基于分?jǐn)?shù)階微分的SIFT算法進(jìn)行比較;并對(duì)算法的效率和最佳微分階次進(jìn)行分析,理論分析和實(shí)驗(yàn)結(jié)果均表明本文算法的有效性。
[Abstract]:Image enhancement and image matching are two basic research contents in the field of image processing and computer vision.Most of the traditional image enhancement and image matching methods are based on integral order calculus, which is not ideal for weak edges and weak textures with weak derivative in images.Fractional differential has the advantages of keeping the weak edge and weak texture details in the image while enhancing the image. However, the existing image enhancement methods based on fractional differential need to find the best order manually.In image matching, the combination of SIFT(Scale Invariant Feature transform algorithm and fractional differential algorithm can extract more feature points in fuzzy image and weak texture image, so the accuracy of matching is improved.However, the choice of optimal differential order still needs manual adjustment, which is time-consuming and laborious.Therefore, this paper focuses on these two problems, the specific work is as follows: 1.By analyzing the effect of fractional differential on signal, the differential order adaptive model in fractional differential image enhancement is constructed. The model is based on the inverse tangent function, the gradient information of the image and the local information entropy.Brightness and contrast are independent variables. The relationship between fractional differential order and image local information is established, so that the best order of each pixel in the image can be automatically calculated according to the local characteristic information of the image.The model is applied to image enhancement of fractional differential Tiansi operator.In order to verify the validity of the model in this paper, several texture images in the standard image library are selected for experiments. The experimental results are qualitatively and quantitatively analyzed, and compared with the second-order differential Laplacian operator, Tiansi operator.Both the theoretical analysis and the experimental results show that the model is effective, and the enhancement effect of the gray image can be continuously changed, which is close to the experimental effect of the best fractional differential order, and accords with the human visual perception.An adaptive fractional order SIFT algorithm is proposed for image matching.Based on the Riemann-Liouvilli R-L-based fractional differential, a new fractional differential mask is designed, which is integrated into the SIFT algorithm to extract more precise and effective key points, thus improving the matching accuracy of the SIFT algorithm.Then, according to the local characteristics of the image, a fractional differential order adaptive mathematical model is constructed.In this model, the relationship between fractional differential order and local feature information is established by using the gradient information of image and the entropy of local information as independent variables, using the inverse tangent function as the prototype, and taking the gradient information of image and the entropy of local information as independent variables.In order to verify the validity of the algorithm and model, we can automatically calculate the best order of each pixel in the image according to the local feature information of the image. In order to verify the validity of the algorithm and the model, we choose the image and the real image in the standard library to carry on the experiment.Compared with the original SIFT algorithm and the fractional differential based SIFT algorithm, the efficiency and optimal differential order of the algorithm are analyzed. The theoretical analysis and experimental results show the effectiveness of the proposed algorithm.
【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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