發(fā)布時間：2018-07-01 15:00

  本文選題：脈沖耦合神經(jīng)網(wǎng)絡(luò) + 圖像融合　； 參考：《中國礦業(yè)大學》2017年碩士論文

【摘要】：脈沖耦合神經(jīng)網(wǎng)絡(luò)(Pulse Coupled Neural Network,PCNN),作為第三代人工神經(jīng)網(wǎng)絡(luò)新模型,被成功應用到圖像處理各領(lǐng)域。脈沖耦合神經(jīng)網(wǎng)絡(luò)模擬了貓等哺乳動物視覺皮層視覺神經(jīng)細胞活動,利用神經(jīng)元的線性相加及非線性相乘調(diào)制耦合兩種特性,并將生物學傳輸?shù)臅r延特性和指數(shù)衰減特性、動物視覺神經(jīng)系統(tǒng)相鄰神經(jīng)元同步激發(fā)產(chǎn)生震蕩特性以及神經(jīng)元處于抑制狀態(tài)時內(nèi)部活動的平衡態(tài)考慮在內(nèi),這樣該模型就更符合真實的生物神經(jīng)網(wǎng)絡(luò)。脈沖耦合神經(jīng)網(wǎng)絡(luò)是單層模型神經(jīng)網(wǎng)絡(luò),不需要訓練過程即可進行特征提取、圖像分割、圖像融合、模式識別等,因此非常適合應用到數(shù)字圖像處理中。本文主要針對脈沖耦合神經(jīng)網(wǎng)絡(luò)在圖像融合領(lǐng)域應用的算法進行探討與改進。研究了遺傳算法優(yōu)化的PCNN應用在非下采樣Contourlet變換(Nonsubsampled Contourlet Transform,NSCT)的圖像融合。脈沖耦合神經(jīng)網(wǎng)絡(luò)在圖像融合中運用廣泛,但是模型中仍存在較多參數(shù)需要憑借人工經(jīng)驗設(shè)置。模型參數(shù)的設(shè)置十分關(guān)鍵,它直接影響融合結(jié)果的好壞,因此本文對PCNN模型參數(shù)的設(shè)置方法進行研究,提出了基于遺傳算法優(yōu)化的PCNN改進模型結(jié)合非下采樣Contourlet變換應用在圖像融合中的新方法。該方法實現(xiàn)了對模型參數(shù)進行自適應設(shè)置,減少了需要憑借人工經(jīng)驗進行設(shè)定參數(shù)的個數(shù),也避免了在參數(shù)選擇過程中的盲目性。通過仿真實驗,驗證了本優(yōu)化算法的可行性。研究了圖像梯度激勵的NSCT-PCNN圖像融合。對PCNN模型進行改進操作,在PCNN中引入圖像梯度的強度及相位相關(guān)性的加權(quán)乘積作為模型的反饋輸入,克服了原始模型中僅利用圖像像素灰度值作為輸入而造成未考慮人眼主觀視覺對圖像局部因素敏感的不足,使脈沖耦合神經(jīng)網(wǎng)絡(luò)與非下采樣Contourlet變換相結(jié)合的融合效果更佳。研究了PCNN應用在二維經(jīng)驗模態(tài)分解(Bidimensional Empirical Mode Decomposition,BEMD)的圖像融合。鑒于PCNN在多尺度圖像分解工具非下采樣Contourlet變換中的成功應用,本文將該神經(jīng)網(wǎng)絡(luò)模型引入到二維經(jīng)驗模態(tài)分解中,提出一種將脈沖耦合神經(jīng)網(wǎng)絡(luò)與圖像壓縮感知相結(jié)合,運用在圖像二維經(jīng)驗模態(tài)分解中的多尺度圖像融合新方法。首先,BEMD將待處理原始圖像分解成多個二維內(nèi)蘊模式函數(shù)(Bidimensional Intrinsic Mode Function,BIMFs)和一個趨勢圖像。然后對BIMFs分別進行壓縮測量,對所得的壓縮測量系數(shù)進行PCNN圖像融合后得到測量采樣BIMFs,再對測量采樣BIMFs進行重構(gòu)得到最終的BIMFs系數(shù)以及對趨勢圖像基于圖像熵權(quán)重的融合得到最終的趨勢圖像系數(shù);最后通過BEMD逆變換可得融合結(jié)果圖像。通過仿真實驗驗證,PCNN與二維經(jīng)驗模態(tài)分解結(jié)合的圖像融合同樣有較佳的效果。
[Abstract]:Pulse coupled neural network (PCNN), as a new model of the third generation artificial neural network, has been successfully applied to various fields of image processing. Impulsive coupled neural network (PNN) is used to simulate the visual nerve cell activity in the visual cortex of mammals such as cats. The linear addition and nonlinear multiplication modulation of neurons are used to couple the neural cells, and the delay characteristics and exponential attenuation characteristics of biological transmission are analyzed. The concussion characteristics of adjacent neurons in the animal visual nervous system and the equilibrium of the internal activities of the neurons in the inhibitory state are taken into account so that the model is more in line with the real biological neural network. Pulse coupled neural network is a single-layer model neural network, which can be used for feature extraction, image segmentation, image fusion and pattern recognition without training, so it is very suitable for digital image processing. In this paper, the algorithm of pulse coupled neural network applied in image fusion is discussed and improved. The application of PCNN optimized by genetic algorithm in image fusion of nonsubsampled Contourlet transform (NSCT) is studied. Pulse coupled neural network is widely used in image fusion, but there are still many parameters in the model that need to be set by artificial experience. The setting of model parameters is very important, which directly affects the quality of fusion results. Therefore, this paper studies the setting method of PCNN model parameters. A new method for image fusion based on genetic algorithm (GA) based on improved PCNN model and non-downsampling Contourlet transform is proposed. This method realizes the adaptive setting of model parameters, reduces the number of parameters that need to be set by human experience, and avoids blindness in the process of parameter selection. The feasibility of the algorithm is verified by simulation experiments. The image fusion of NSCT-PCNN with image gradient excitation is studied. The PCNN model is improved and the weighted product of the intensity and phase correlation of the image gradient is introduced into PCNN as the feedback input of the model. It overcomes the shortcoming that only the gray value of image pixels is used as input in the original model, which does not consider the sensitivity of human subjective vision to local factors of the image, and makes the fusion effect of pulse coupled neural network and non-downsampling Contourlet transform better. The image fusion of Bidimensional empirical Mode decomposition (BEMD) based on PCNN is studied. In view of the successful application of PCNN in the non-downsampling Contourlet transform of multi-scale image decomposition tool, this paper introduces the neural network model into two-dimensional empirical mode decomposition, and proposes a new method which combines pulse coupled neural network with image compression perception. A new multi-scale image fusion method in image two-dimensional empirical mode decomposition (EMD) is proposed. First, BEMD decomposes the original image into two dimensional intrinsic mode BIMFs and a trend image. Then the compression measurements of BIMFs are carried out, After the compression measurement coefficients are fused with PCNN images, the BIMFs are obtained, the final BIMFs coefficients are reconstructed from the measured samples, and the trend image coefficients are obtained by fusion based on the entropy weight of the trend images. Finally, the fused image can be obtained by BEMD inverse transform. The simulation results show that the image fusion based on PCNN and two-dimensional empirical mode decomposition is also effective.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP18

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