针对部分对比度低、噪声大的图像，提出一种基于大脑内部生成机制(IGM)和改进脉冲耦合神经网络(PCNN)的图像增强方法。首先，根据IGM有关理论将原始图像分解为细节子图与粗糙子图；然后，采用改进的PCNN增强方法对粗糙子图进行处理，以提高整体对比度，采用PCNN与模糊集理论结合的增强方法对细节子图进行处理，增强边缘等细节信息并去除部分噪声；最后，将处理后的细节子图与粗糙子图重构，得到最终的增强图像。实验结果表明，该方法能够有效增强图像的对比度和纹理细节，减少部分噪声，较好地保留原图细节信息。

To deal with low-contrast and high-noisy natural images, an image enhancement method based on internal generative mechanism (IGM) and improved pulse coupled neural network (PCNN) is proposed. First, the original image is decomposed into rough sub-graph and detail sub-graph by the theory of IGM. And then, an im-proved PCNN method is adopted to make the rough sub-graph more clearly. At the same time, the enhancement method which PCNN incorporates with fuzzy sets is introduced for the detail sub-graph so as to sharpen the im-age edge and remove outliers. Finally, the final image is reconstructed by processed rough sub-graph and detail sub-graph. Experimental results show that the proposed algorithm can effectively enhance the image contrast and image contour, as well as filter out some noise without any loss of image edges.