【摘要】 构造了具有抗噪性能的自适应Bandelet基。文中首先在带噪信号的二进剖分块内,利用软阈值函数和在SURE准则下获得的阈值T,计算出对无噪信号的逼近结果,再按照最小化逼近误差原则,寻找出二进剖分块的几何方向。然后在最小化均分误差(MMSE)的原则下合并二进剖分块,最终确定了NA-Bandelet的方向。提出了基于NA-Bandelet基去噪算法,分别对光学图像和SAR图像进行去噪和降斑处理。与基于正交小波基、Bandelet基的去噪、降斑算法相比较,结果表明,提出的算法能有效地去除噪声,并且具有更强的边缘保持能力。更多还原

【Abstract】 A new class of noiseproof adaptive Bandelet bases (NA-Bandelet bases) is proposed. First, by employing soft-threshold function and the threshold obtained by SURE principle, the approximation of the original image in each dyadic square is computed, and then the geometrical direction in each dyadic square is found out according to the principle of the minimal approximation error. Secondly, the small dyadic square is combined based on the principle of Minimum Mean Square Error (MMSE), so the directions of NA-Bandelet bases are determined at last. Therefore, a novel denoising method based on the NA-Bandelet bases is presented, and it is applied to denoise and despeckle the optical image and SAR image respectively. Compared with the denoising methods based on Orthonormal wavelet bases and Bandelet bases, the experiment result shows that the algorithm proposed in the paper can reduce noise more effectively with improved edge-preserving ability.更多还原