在处理由椒盐噪声污染的高对比度图像时,使用传统的三维块匹配算法(Block-Matching and 3D filtering,BM3D)去噪不能有效保留图像的边缘和纹理细节,在图像的边缘会出现边缘振铃效应。为了改善传统BM3D算法在处理椒盐噪声时的不足,提出了用边缘方向代替水平方向搜索相似块的BM3D改进去噪算法。实验结果表明,改进BM3D算法获得的相似块数量是传统BM3D算法的3倍,峰值信噪比(PSNR)也得到进一步提高,在去除椒盐噪声的同时也使图像边缘得到有效保留。
A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
