摘要
为了解决方向对纹理图像细节增强的限制问题,提出一种融合小波变换与改进脉冲耦合神经网络(PCNN)的图像增强算法。该算法首先对图像进行二维离散小波变换,提取图像的高频分量图。然后将图像像素的局部梯度值作为链接强度系数,在动态阈值函数中加入侧抑制信号来改进脉冲耦合神经网络;并用改进的脉冲耦合神经网络对高频分量图进行增强。最后使用中值滤波对小波重构后的图像进行非线性平滑,实现纹理图像细节的增强。实验结果表明,该算法能够有效地减少图像细节增强时方向的限制。增强后,纹理图像的细节更加丰富,整体对比度也有一定的提高。
To solve the restriction problem of direction for the detail enhancement of texture image, this paper proposes an image enhancement algorithm which merges wavelet transform and improved Pulse Coupled Neural Network（PCNN）. Firstly, the image is decomposed by two-dimension discrete wavelet transform to extract the high-frequency component diagram. Then, it improves pulse coupled neural network which uses the local gradients value of image pixel as the link intensity coefficient and adds the lateral inhibition signal to dynamic threshold function, meanwhile, the high-frequency component diagram is enhanced by the improved pulse coupled neural network. Finally, the reconstructed image by inverse wavelet transform is smoothed nonlinearly by median filtering and enhancement for details of texture image is implemented. The experimental results show that this algorithm can effectively reduce the restriction problem of direction for detail enhancement of image. The details of texture image are more plentiful and the overall contrast has a certain improvement after the texture image is enhanced by this algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2016年第8期163-168,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.41301480)
西安石油大学全日制硕士研究生创新基金(No.2014cx130635)
关键词
纹理图像
图像增强
小波变换
脉冲耦合神经网络
中值滤波
texture image
image enhancement
wavelet transform
Pulse Coupled Neural Network（PCNN）
median filtering
