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中文摘要：
现有脉冲耦合神经网络模型普遍存在阂值函数复杂、用于图像平滑时图像信息易丢失以及易产生污斑等缺陷．为此，文中设计了一种阈值线性衰减的输出带权均值型PCNN模型，简称L＆A—PCNN．通过数学推理和实验获得了L＆A-PCNN的关键参数的最优选取范围，并将L＆A—PCNN与中值滤波器结合对图像去噪领域的难点——混合噪声进行修复．仿真实验结果证明，L＆A—PCNN算法的去噪性能比现有算法提高了5％～30％．
英文摘要：
As the existing pulse-coupled neural network （PCNN） suh in blur patch and information loss during image smoothing, a models are of complex threshold functions and may remodified PCNN model L＆A-PCNN with linear-attenuated threshold and weighted average gray level output is designed. The optimal value ranges of the key parameters of the new model are then determined via mathematical reasoning and experiments. Moreover, the mixed noise which is difficult to denoise is recovered by combining the L＆A-PCNN model with a median filter. Simulated results show that the denoising performance of the new algorithm improves by 5% -30%, as compared with the existing algorithms.
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