本文選題：視覺顯著性 + 復(fù)雜背景��； 參考：《華中科技大學(xué)》2016年碩士論文

【摘要】：近年來,顯著性研究被成功地應(yīng)用在目標(biāo)檢測、識(shí)別、圖像壓縮等多個(gè)領(lǐng)域,這些領(lǐng)域中就包括了紅外小目標(biāo)檢測。作為紅外自尋的制導(dǎo)、搜索跟蹤和預(yù)警等領(lǐng)域的一項(xiàng)關(guān)鍵技術(shù),紅外小目標(biāo)檢測,特別是復(fù)雜云干擾下的空中目標(biāo)、海雜波與云層干擾下海天背景的紅外小目標(biāo)、以及復(fù)雜地面背景下小目標(biāo)檢測依然是目前的研究熱點(diǎn)和難點(diǎn)。本文在全面總結(jié)前人工作的基礎(chǔ)上,對復(fù)雜背景下紅外小目標(biāo)圖像的特征提取、視覺顯著性模型的構(gòu)建等展開了深入研究。論文的主要工作如下:首先,介紹了紅外小目標(biāo)圖像的數(shù)學(xué)模型,分析了圖像目標(biāo)、背景和噪聲的特性,通過預(yù)處理增加了背景與目標(biāo)的對比度、抑制了圖像的噪聲。其次,在紅外小目標(biāo)圖像數(shù)學(xué)模型的指導(dǎo)下,提出了一種新的基于頻譜殘差法的顯著目標(biāo)檢測算法,該方法將經(jīng)典的頻域顯著性方法與兩個(gè)一階梯度方向特征結(jié)合,提高了算法的抗噪性和準(zhǔn)確性。然后,針對頻譜殘差方法存在的缺陷,提出了一種基于超復(fù)數(shù)幅度譜的紅外小目標(biāo)檢測算法,研究了幅度譜與重復(fù)的非顯著性區(qū)域的對應(yīng)關(guān)系,通過結(jié)合圖像二階方向?qū)?shù)特征與灰度特性,將紅外圖像重構(gòu)為超復(fù)數(shù)矩陣形式,進(jìn)而使用高斯核對超復(fù)數(shù)傅里葉變換后的幅度譜進(jìn)行濾波,抑制非顯著性區(qū)域,使得目標(biāo)這一顯著性區(qū)域獲得增強(qiáng)效果。經(jīng)過實(shí)驗(yàn)驗(yàn)證,該算法具有更強(qiáng)的目標(biāo)增強(qiáng)與背景抑制能力。最后,對于顯著性圖的分割問題,提出了兩種思路,一種是傳統(tǒng)的基于閾值分析的方法,另一種是基于模糊度量的閾值分割方法,實(shí)驗(yàn)結(jié)果證明了前一種算法更高效,準(zhǔn)確性也有保證。
[Abstract]:In recent years, the significance research has been successfully applied in many fields, such as target detection, recognition, image compression and so on. These fields include infrared small target detection. As a key technology in infrared homing guidance, search tracking and early warning, infrared small target detection, especially in the sky under complex cloud interference, sea clutter and cloud interference, is a key technology in the field of ocean and sky background. And small target detection in complex ground background is still a hot and difficult point. On the basis of summing up the previous work, this paper makes an in-depth study on the feature extraction of infrared small target images and the construction of visual salience model in complex background. The main work of this paper is as follows: firstly, the mathematical model of infrared small target image is introduced, and the characteristics of image object, background and noise are analyzed. The contrast between background and target is increased by preprocessing, and the noise of image is suppressed. Secondly, under the guidance of the mathematical model of infrared small target image, a new significant target detection algorithm based on spectral residual method is proposed, which combines the classical frequency-domain saliency method with two first-order gradient directional features. The noise resistance and accuracy of the algorithm are improved. Then, aiming at the defects of spectrum residual method, an infrared small target detection algorithm based on hypercomplex amplitude spectrum is proposed, and the corresponding relationship between amplitude spectrum and repetitive non-significant region is studied. The infrared image is reconstructed into a hypercomplex matrix form by combining the second-order directional derivative and gray characteristics of the image, and then the amplitude spectrum of the super-complex Fourier transform is filtered by using Gao Si check to suppress the non-significant region. Increases the target's significant area. Experimental results show that the algorithm has stronger ability of target enhancement and background suppression. Finally, for the segmentation of salient graph, two methods are proposed, one is the traditional method based on threshold analysis, the other is the threshold segmentation method based on fuzzy metric. The experimental results show that the former algorithm is more efficient. Accuracy is also guaranteed.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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本文編號(hào)：1973942