混合噪聲濾除論文：基于改進型PCNN的圖像混合噪聲濾除方法

1、 混合噪聲濾除論文：基于改進型PCNN的圖像混合噪聲濾除方法【中文摘要】圖像在形成和傳輸?shù)倪^程中不可避免的會受到噪聲的污染,從而對圖像的視覺效果和后續(xù)處理帶來不利的影響,甚至直接影響到后續(xù)處理的效果。在實際的應(yīng)用中,圖像往往不止受到一種噪聲的影響,而是受到多種噪聲構(gòu)成的混合噪聲的影響,所以對圖像混合噪聲進行濾波處理具有十分重要的意義。脈沖耦合神經(jīng)網(wǎng)絡(luò)(Pulse Coupled Neural Network, PCNN)是近年來提出的通過模擬動物大腦皮層視神經(jīng)元同步發(fā)放脈沖現(xiàn)象而建立的一種新型神經(jīng)網(wǎng)絡(luò)模型。由于其特有的生物學(xué)背景及特性,已成功的被應(yīng)用于圖像濾波、圖像分割、特征提取、目標(biāo)識別等領(lǐng)

2、域。由于PCNN應(yīng)用于圖像處理時有模型簡單,容易用集成電路實現(xiàn),使的圖像的實時處理成為可能。因此,基于PCNN的混合噪聲濾除方法的研究是一個非常有意義的研究課題。本文首先對脈沖耦合神經(jīng)網(wǎng)絡(luò)模型的基本理論和運行機制進行了詳細的闡述,并分析和總結(jié)了它的結(jié)構(gòu)特點和特性。在借鑒已有的理論成果和思想的基礎(chǔ)上,將PCNN模型進行了適當(dāng)?shù)暮喕透倪M,并對改進模型的運行機制和參數(shù)設(shè)置進行了詳細的分析。其次,對由脈沖噪聲和高斯噪聲構(gòu)成的混合噪聲的特點進行分析,將改進PCNN模型與中值濾波結(jié)合應(yīng)用到灰度圖像的混合噪聲濾波中。并在改進的PCNN模型的基礎(chǔ)上,提出了一種模型網(wǎng)絡(luò)參數(shù)自適應(yīng)生成機制,解決了需要通過人工調(diào)

3、制模型網(wǎng)絡(luò)參數(shù)才能達到最優(yōu)濾波效果的問題。通過仿真實驗對比,自適應(yīng)生成網(wǎng)絡(luò)參數(shù)的濾波效果要優(yōu)于人工調(diào)制網(wǎng)絡(luò)參數(shù)的濾波效果,在具有較好的濾波效果的同時,也很好的保留圖像的細節(jié)。另外,本文還將改進型PCNN濾波算法與中值濾波和模糊規(guī)則濾波算法進行對比實驗,經(jīng)比較改進型PCNN模型濾波算法均優(yōu)于兩種濾波算法,隨著圖像受到的混合噪聲程度的增加,優(yōu)勢越明顯。最后,針對彩色圖像的混合噪聲,提出了一種在RGB色彩空間的基于改進PCNN模型的彩色圖像混合噪聲濾波方法,通過大量實驗證明,本文提出的方法能夠很好的濾除彩色圖像混合噪聲?！居⑽恼縄n the process of image forming an

4、d transmission, image is inevitably polluted by noise which will have an adverse impact on image visual effect and subsequent processing, even directly affect the follow-up image processing results. In actual applications, image usually is polluted by mixture-noise that is composed of different kind

5、s of noise, rather than a kind of noise. So mixed-noise filtering of image is of great significance.Pulse coupled neural network (PCNN) is a new kind of neural network model proposed recently by simulating the optic neuron of animal cerebral cortex. Because of its unique biological background and ch

6、aracteristics, has successfully been applied to image filtering, image segmentation, feature extraction, object recognition etc. As PCNN model is simple to apply to image processing and easy to use integrated circuits to implement, so that it is possible to real-time image processing. Therefore, the

7、 research of method of mixed noise filtering base on PCNN is a very interesting research topic.This paper firstly elaborates on the basic theory and operating mechanism of PCNN, analyzes and summarizes its structure features and characteristics. Simplified and modified PCNN model appropriately on th

8、e foundation of referring existent theory and ideas, it is analyzed in detail the operating mechanism and parameter settings of the modified model.Secondly, this paper analyzes the characteristic of mixed noise constituted by pulse noise and Gauss noise and denoises mixed-noise in gray-scale image u

9、sing a modified PCNN model combined with median filtering. Generally, we need to manually choose an appropriate network model parameter to achieve an optimal filtering result. In order to eliminate this trivial task, this paper proposes automatic mechanism to adaptively generate an optimal value for

10、 the network model parameter based on our modified model. Simulation results demonstrate that the denoising performance of our automatic mechanism is better than conventional algorithms that using manually parameter choosing. Meanwhile, our method is able to preserve the details in images.Also, this

11、 paper compares the proposed modified PCNN filtering algorithm to median filtering and fuzzy rule based filtering algorithm. The results show that the filtering performance of our algorithm is better than the others. In particular, our algorithm can achieve a more obvious improvement as the mixed-no

12、ise level of the images is getting higher.Finally, concerning mixed-noise denoising in color images, this work proposes a mixture noise filtering approach for color images in RGB color space based on the modified PCNN model. A large number of experiments show that the modified PCNN filtering algorit

13、hm can denoise the mixed-noise in color images effectively.【關(guān)鍵詞】混合噪聲濾除 脈沖耦合神經(jīng)網(wǎng)絡(luò) 自適應(yīng) 灰度圖像 彩色圖像【英文關(guān)鍵詞】mixed-noise removal PCNN adaptive gray-scale image color image【目錄】基于改進型PCNN的圖像混合噪聲濾除方法摘要5-6Abstract6-7第一章 緒論10-151.1 研究背景與意義10-121.2 圖像去噪研究現(xiàn)狀12-131.3 本文的主要內(nèi)容及組織結(jié)構(gòu)13-15第二章 脈沖耦合神經(jīng)網(wǎng)絡(luò)的基本理論15-212.1 脈沖耦合神經(jīng)元

14、模型15-182.1.1 接收部分16-172.1.2 非線性調(diào)制部分172.1.3 脈沖產(chǎn)生部分17-182.2 PCNN的運行機制182.3 PCNN的特性18-202.4 本章小結(jié)20-21第三章 脈沖耦合神經(jīng)網(wǎng)絡(luò)的改進模型21-333.1 改進模型的基本結(jié)構(gòu)21-243.1.1 接收部分22-233.1.2 非線性調(diào)制部分233.1.3 脈沖產(chǎn)生部分23-243.2 改進模型的行為分析24-283.2.1 單個神經(jīng)元24-253.2.2 兩個神經(jīng)元25-273.2.3 多個神經(jīng)元27-283.3 改進模型網(wǎng)絡(luò)參數(shù)分析28-323.3.1 鏈接權(quán)值矩陣W29-303.3.2 鏈接強度系數(shù)30-313.3.3 動態(tài)閾值幅度系數(shù)V_k31-323.4 本章小結(jié)32-33第四章 改進型PCNN模型對灰度圖像混合噪聲的濾除33-474.1 混合噪聲的構(gòu)成及特點分析33-374.1.1 脈沖噪聲的特點34-354.1.2 高斯噪聲的特點35-364.1.3 混合噪聲的特點36-374.2 改進PCNN模型在圖像噪聲濾除中的應(yīng)用37-424.2.1 模型參數(shù)設(shè)置38-414.2.2 圖像中強噪聲點的判斷41-424.2.3 算法步驟424.3 實驗結(jié)果及