本文選題：圖像去噪 + 圖像增強(qiáng)��； 參考：《寧夏大學(xué)》2017年碩士論文

【摘要】：圖像分割作為圖像分析與處理的關(guān)鍵,對(duì)圖像的邊緣目標(biāo)提取具有重要影響。Otsu分割方法是常用的圖像分割方法之一,應(yīng)用范圍廣泛。本文針對(duì)傳統(tǒng)Otsu算法運(yùn)行時(shí)間長(zhǎng)、計(jì)算復(fù)雜且優(yōu)化Otsu算法的傳統(tǒng)粒子群算法收斂速度慢、容易陷入局部最優(yōu)的缺點(diǎn),提出了用分?jǐn)?shù)階微積分算法優(yōu)化粒子群算法的方案,將Otsu算法、分?jǐn)?shù)階微積分算法、粒子群算法三種算法進(jìn)行結(jié)合并改進(jìn),并將新算法應(yīng)用于圖像分割。本文主要完成的工作和內(nèi)容如下:(1)在圖像分割之前,結(jié)合分?jǐn)?shù)階幅頻特性曲線以及分?jǐn)?shù)階微積分在圖像去噪、增強(qiáng)處理中的優(yōu)越性,針對(duì)不同特征的圖像不適合用相同的分?jǐn)?shù)階次來(lái)處理,且分?jǐn)?shù)階次需要人為設(shè)定的缺點(diǎn),提出了一種自適應(yīng)分?jǐn)?shù)階微積分的圖像去噪、增強(qiáng)算法。根據(jù)圖像中像素點(diǎn)的紋理、噪聲強(qiáng)弱應(yīng)采用不同分?jǐn)?shù)階次處理的特點(diǎn),結(jié)合分?jǐn)?shù)階次在一定范圍內(nèi)能夠取得較好效果的特點(diǎn),提出了不同梯度值下的分?jǐn)?shù)階次自適應(yīng)公式,以確保對(duì)于梯度值大的噪聲強(qiáng)點(diǎn),取較小的負(fù)階次;對(duì)于梯度值小的噪聲弱點(diǎn),取較大的負(fù)階次;對(duì)于梯度值大的圖像邊緣,取較大的正階次;對(duì)于梯度值小的圖像紋理,取較小的正階次。針對(duì)不同類(lèi)型圖像,采用自適應(yīng)階次的分?jǐn)?shù)階微分、積分對(duì)待分割圖像實(shí)現(xiàn)去噪、增強(qiáng)預(yù)處理。(2)在深入分析傳統(tǒng)Otsu算法、分?jǐn)?shù)階微積分算法及粒子群優(yōu)化算法理論的基礎(chǔ)上,將三種算法結(jié)合并改進(jìn),提出了分?jǐn)?shù)階粒子群Otsu圖像閾值分割(ImFpsoOtsu)算法。首先采用灰度級(jí)-梯度二維直方圖算法,以O(shè)tsu算法的最大類(lèi)間方差為適應(yīng)度函數(shù)。然后通過(guò)引入粒子進(jìn)化因子,利用粒子的進(jìn)化信息自適應(yīng)更改分?jǐn)?shù)階次α,同時(shí)通過(guò)速度增量為零來(lái)更新粒子速度、位置值。最后結(jié)合傳統(tǒng)粒子群粒子更新公式并采用粒子對(duì)稱(chēng)分布的改進(jìn)粒子群算法獲取最佳閾值,將目標(biāo)從圖像中分割出來(lái)。分?jǐn)?shù)階粒子群Otsu算法,最終實(shí)現(xiàn)了圖像的有效分割,解決了傳統(tǒng)的粒子群優(yōu)化算法陷入局部最優(yōu)的問(wèn)題,提高了收斂速度。實(shí)驗(yàn)結(jié)果表明,本文提出的自適應(yīng)分?jǐn)?shù)階次的圖像預(yù)處理算法,從主觀視覺(jué)和客觀的信噪比、熵值上優(yōu)于傳統(tǒng)算法,即圖像去噪、增強(qiáng)效果更好�；诜�?jǐn)?shù)階粒子群的Otsu算法,從視覺(jué)效果和適應(yīng)度曲線收斂程度驗(yàn)證了本文算法在保證分割精度的同時(shí),收斂速度更快。
[Abstract]:As the key of image analysis and processing, image segmentation has an important impact on edge target extraction. Otsu segmentation method is one of the commonly used image segmentation methods, and has a wide range of applications.Aiming at the disadvantages of the traditional Otsu algorithm, such as long running time, complex computation and low convergence speed and easy to fall into local optimum, a scheme of particle swarm optimization based on fractional calculus algorithm is proposed in this paper, in which the convergence speed of the traditional particle swarm optimization algorithm is slow and the algorithm is easy to fall into the local optimum.The Otsu algorithm, fractional calculus algorithm and particle swarm optimization algorithm are combined and improved, and the new algorithm is applied to image segmentation.The main work and contents of this paper are as follows: before image segmentation, combining fractional order amplitude-frequency characteristic curve and fractional calculus in image denoising, enhancing the superiority of image processing,An adaptive image denoising and enhancement algorithm based on fractional calculus is proposed to solve the problem that the image with different features is not suitable for processing with the same fractional order and the fractional order needs to be set artificially.According to the texture of pixels in the image, the noise intensity should be processed by different fractional order, combined with the characteristic that fractional order can get better effect in a certain range, a fractional order adaptive formula with different gradient values is put forward.In order to ensure that the negative order is smaller for the noise intensity point with large gradient value, the negative order is larger for the noise weakness with small gradient value, the larger positive order is taken for the image edge with large gradient value, and the image texture with small gradient value is obtained.Take a smaller positive order.According to different types of images, using fractional differential of adaptive order, integral treatment of segmented image to achieve denoising, enhancement preprocessing.) based on the in-depth analysis of the traditional Otsu algorithm, fractional calculus algorithm and particle swarm optimization theory.By combining and improving the three algorithms, a fractional order particle swarm optimization (Otsu) algorithm for threshold segmentation of Otsu images is proposed.Firstly, the gray-grads two-dimensional histogram algorithm is used, and the maximum inter-class variance of the Otsu algorithm is taken as the fitness function.Then, by introducing the particle evolution factor, the fractional order 偽 is changed adaptively by using the evolution information of the particle, and the particle velocity and position value are updated by increasing the velocity to zero.Finally, combined with the traditional particle updating formula and the improved particle swarm optimization algorithm of particle symmetry distribution, the optimal threshold is obtained, and the target is segmented from the image.The fractional-order particle swarm optimization (Otsu) algorithm realizes the effective segmentation of the image, solves the problem that the traditional particle swarm optimization algorithm falls into the local optimum, and improves the convergence speed.The experimental results show that the proposed adaptive fractional order image preprocessing algorithm is superior to the traditional algorithm in terms of subjective vision and objective SNR, I. e., image denoising, and the enhancement effect is better.The Otsu algorithm based on fractional particle swarm optimization verifies that the proposed algorithm can guarantee the segmentation accuracy and converge faster from the visual effect and the convergence degree of fitness curve.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 袁小翠;吳祿慎;陳華偉;;基于Otsu方法的鋼軌圖像分割[J];光學(xué)精密工程;2016年07期

2 孟亞州;馬瑜;白冰;高林爽;;基于粒子群優(yōu)化的Otsu肺組織分割算法[J];液晶與顯示;2015年06期

3 白冰;馬瑜;楊曉麗;孟亞州;魏晶茹;;基于二維粒子群優(yōu)化的圖像模糊增強(qiáng)算法研究[J];電視技術(shù);2015年19期

4 王軍敏;姬鵬飛;;基于微分算子的邊緣檢測(cè)及其應(yīng)用[J];平頂山學(xué)院學(xué)報(bào);2015年02期

5 鄧長(zhǎng)虹;馬慶;肖永;游佳斌;李世春;;基于自學(xué)習(xí)遷移粒子群算法及高斯罰函數(shù)的無(wú)功優(yōu)化方法[J];電網(wǎng)技術(shù);2014年12期

6 錢(qián)生;陳宗海;林名強(qiáng);張陳斌;;基于條件隨機(jī)場(chǎng)和圖像分割的顯著性檢測(cè)[J];自動(dòng)化學(xué)報(bào);2015年04期

7 馬瑜;余婷;趙九龍;李曉輝;張寧寧;;一種改進(jìn)的自適應(yīng)分?jǐn)?shù)階積分圖像去噪算法[J];電視技術(shù);2014年19期

8 王永興;蒲亦非;鞏曉倩;周激流;;分?jǐn)?shù)階三維塊匹配去噪算法[J];計(jì)算機(jī)應(yīng)用研究;2015年01期

9 田丹;薛定宇;楊雅婕;;分?jǐn)?shù)階原始對(duì)偶去噪模型及其數(shù)值算法[J];中國(guó)圖象圖形學(xué)報(bào);2014年06期

10 譚璐蕓;;分?jǐn)?shù)階微積分在現(xiàn)代信號(hào)分析處理中的應(yīng)用[J];遼寧科技學(xué)院學(xué)報(bào);2014年02期

相關(guān)博士學(xué)位論文 前1條

1 蒲亦非;分?jǐn)?shù)階微積分在現(xiàn)代信號(hào)分析與處理中應(yīng)用的研究[D];四川大學(xué);2006年

，

本文編號(hào)：1772502