一種自適應(yīng)PCNN多聚焦圖像融合新方法
A Novel Algorithm of Multi-focus Image Fusion Using Adaptive PCNN
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摘要: 該文通過(guò)分析脈沖耦合神經(jīng)網(wǎng)絡(luò)(PCNN)參數(shù)模型，結(jié)合多聚焦圖像的基本特點(diǎn)和人眼視覺特性，提出了一種自適應(yīng)PCNN多聚焦圖像融合的新方法。該方法使用圖像逐像素的清晰度作為PCNN對(duì)應(yīng)神經(jīng)元的鏈接強(qiáng)度，經(jīng)過(guò)PCNN點(diǎn)火獲得每幅參與融合圖像的點(diǎn)火映射圖，再通過(guò)判決選擇算子，判定并選擇各參與融合圖像中的清晰部分生成融合圖像。該方法中，其它參數(shù)如閾值調(diào)整常量等對(duì)于融合結(jié)果影響很小，解決了PCNN方法的參數(shù)調(diào)整困難的問(wèn)題。實(shí)驗(yàn)結(jié)果表明，該方法的融合效果優(yōu)于小波變換方法和Laplace塔型方法。Abstract: The proposed new fusion algorithm is based on the improved Pulse Coupled Neural network(PCNN) model, the fundamental characteristics of multi-focus images and the properties of human vision system. Compared with the traditional algorithm where the linking strength of each neuron is the same and its value is chosen through experimentation, this algorithm uses the sharpness of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image taking part in the fusion. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. Furthermore, by this algorithm, other parameters, for example, , the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do in multi-focus image fusion.
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