本文關(guān)鍵詞： Gabor濾波器 超聲圖像 邊緣檢測 邊緣增強(qiáng)　出處：《內(nèi)蒙古大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：本文的主要研究內(nèi)容是對B超圖像和聲吶圖像的邊緣進(jìn)行增強(qiáng)與檢測。在醫(yī)學(xué)領(lǐng)域,醫(yī)生依靠對超聲圖像的觀察來判斷患者病情;在海洋領(lǐng)域,聲吶員通過對聲吶圖像的分析來探究海洋。但是一般B超圖像和聲吶圖像的邊緣分辨率低、品質(zhì)惡劣,而圖像的邊緣又是圖像中的重要信息,如果不進(jìn)行處理,會嚴(yán)重影響研究者的判斷。因此,本文結(jié)合B超圖像和聲吶圖像的特點并根據(jù)現(xiàn)有的邊緣增強(qiáng)和邊緣檢測的理論,進(jìn)一步探究基于Gabor濾波器的超聲圖像的邊緣增強(qiáng)和邊緣檢測的方法。為解決直方圖均衡化中增強(qiáng)的對象是圖像的全局而不是圖像中的目標(biāo)本文使用將直方圖動態(tài)分割為多個子直方圖的方法,并且提出了在直方圖曲線的波峰和波谷1/2處劃分的方法;為解決直方圖均衡化在增強(qiáng)過程中圖像中高頻灰度數(shù)量增加、低頻灰度數(shù)量壓縮會導(dǎo)致圖像出現(xiàn)不自然的過度增強(qiáng)現(xiàn)像,本文又將分割完的子直方圖進(jìn)行了自適應(yīng)三角形的對比度增強(qiáng)算法(Adaptive Trilateral Contrast Enhancement,ATCE)。這樣,既使直方圖均衡化的方法具有目標(biāo)性,又減輕了因灰度級分配不均勻而導(dǎo)致的過度增強(qiáng)現(xiàn)象。在實現(xiàn)圖像邊緣銳化的方法中,本文使用了修正的Gabor濾波器(modified Gabor filter,MGF)與高頻補(bǔ)償濾波器相結(jié)合的方法。Gabor濾波器的方向和頻域表達(dá)類似于人眼系統(tǒng),并且符合海森堡測不準(zhǔn)原則所判定的有效時間與有效頻率帶寬兩者乘積的下限。但Gabor濾波器獲得的頻率和方向有限得到的邊緣信息模糊且殘缺不全,本文通過使用具有雙波峰的MGF使檢測出的邊緣更清晰更完整,再利用高頻補(bǔ)償濾波器對圖像的邊緣進(jìn)一步銳化和去噪。在實現(xiàn)圖像邊緣檢測的方法中,本文提出了基于矩形核Gabor濾波器(Rectangular Nuclear Gabor Filter,RNGF)的邊緣檢測方法,該方法兼具矩形核對方向判斷準(zhǔn)確的優(yōu)勢和Gabor濾波器自身的優(yōu)勢,在一定程度上解決了使用矩形核高斯函數(shù)的邊緣檢測方法易出現(xiàn)噪聲的問題。
[Abstract]:The main content of this paper is the enhancement and detection of ultrasound images and sonar image edge. In the field of medicine, doctors rely on observation of ultrasound images to determine the patient's condition; in the field of marine sonar who, through the analysis of the sonar image to explore the ocean. But the general B images and sonar image resolution is low. Poor quality, and the edge of the image is an important information in the image, if it is not treated, it will seriously affect the researcher's judgment. Therefore, this paper combined with the characteristics of ultrasound images and the sonar image and according to the existing edge enhancement and edge detection theory, to further explore the method of ultrasound image edge enhancement and Gabor filter based on edge detection. In order to solve the problem of object enhanced histogram equalization is the image of the whole and not the object in the image is used in this paper will be divided into a plurality of sub dynamic histogram The histogram method, and put forward a method in the histogram curve peaks and trough 1/2 division; in order to solve the problem of histogram equalization in high frequency enhanced gray image in the process of quantity increase, low frequency gray compression will lead to excessive number of image enhancement is not a natural phenomenon, this paper will end by sub histogram contrast adaptive enhancement algorithm (Adaptive Trilateral Contrast Enhancement triangle, ATCE). In this way, even if the method of histogram equalization is targeted, but also reduce the gray level due to uneven distribution caused by excessive enhancement. In method of image edge sharpening, we use a modified Gabor filter (modified Gabor filter. MGF) direction and frequency domain method.Gabor filter combined with high frequency compensation filter expression similar to the human system, and in line with the original Heisenberg uncertainty The lower limit of effective time is determined and the effective frequency bandwidth of both product. But the Gabor filter to obtain the frequency and direction of the finite fuzzy edge information and incomplete, this paper through the use of double peak MGF has the edge detected is clearer and more complete, with high frequency compensation filter for image edge sharpening and go further noise. The implementation method of image edge detection, this paper presents rectangular filter based on nuclear Gabor (Rectangular Nuclear Gabor Filter, RNGF) edge detection method, this method has advantages of accurate direction of rectangular check and Gabor filter their own advantages, to a certain extent to solve the edge detection method using rectangular kernel Gauss function prone to noise problems.

【學(xué)位授予單位】：內(nèi)蒙古大學(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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本文編號：1455683