基于HMT模型层间映射的图像邻域去噪算法

Image de-noising method based on HMT model interlayer mapping in wavelet domain

本文提出了一种以图像分割为基础的图像去噪算法。本文算法根据图像自身的性质,利用脉冲耦合神经网络模型自适应地将小波分解后的低频图像分割成不同的区域,并且利用简化的HMT层间模型在离散和平稳小波分别处理的情况下,将得到的连通区域邻域映射到各个不同的高频子带上。进一步结合固定的窗口,作为邻域去噪算法中的邻域。实验结果表明,该方法在降低了图像噪声的同时又尽可能地保留了图像的边缘信息,是一种有效的去噪方法。

A new image de-noising algorithm based on image segmentation is proposed to keep image edges more effectively. The proposed method segments the low-frequency subband into many domains adaptively by PCNN,and treats the connected regions gotten as neighbourhood.With a simplified HMT model in both discrete and stationary wavelet,the quad-tree inter-layer model is used to map the neighbourhood into the high-frequency subbands.And the regions gotten are taken as irregular neighborhoods for denoising. Further it chooses coefficients both in irregular neighborhood and in a fixed rectangular window,and selects the coefficients have closer geometric distance.A better restoration of images is demonstrated in the results of experiments,with detail of images kept as well as image noises decreasing.