发表机构
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences; Wireless Technology Center, Purdue University; Department of Mathematics, Iowa State University（保加利亚科学院信息与通信技术研究所; 普渡大学无线技术中心; 爱荷华州立大学数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文采用快速Bauer矩阵谱分解方法构造两种新型超紧支撑正交多小波，其性能优于GHM等多种多滤波器，在图像压缩与去噪等应用中表现更优。
AI 中文摘要
本文针对正交CL多小波滤波器的矩阵乘积滤波器，采用快速Bauer矩阵谱分解方法，构造了两种具有超紧支撑的新型正交多小波。这两种多小波具备正交性、对称性/反对称性，其中一种相比其他超紧支撑多小波具有更优的编码性能与光滑性。将新型多小波滤波器在基于子带的边缘检测、灰度与彩色图像压缩、一维及二维信号去噪中的性能，与GHM、SA4、CL、整数Haar及Alpert多滤波器进行对比分析，结果显示新型多小波在图像压缩与去噪应用中，可提供更优的人类视觉感知指标、SSIM及MS-SSIM。
英文摘要
The paper considers the construction of two new orthogonal multiwavelets with supercompact support by using the Fast Bauer's method for matrix spectral factorization on the matrix product filter of the orthogonal CL multiwavelet filter. The new multiwavelets possess orthogonality, symmetry/antisymmetry, and one of them provides better coding and smoothness than other supercompact multiwavelets. The performance of the new multiwavelet filters in subband-based edge detection, grayscale and color image compression and 1D and 2D signal denoising is compared with the GHM, SA4, CL, Integer Haar and Alpert multifilters. The comparative analysis shows that new multiwavelets can provides better human visual measures, SSIM and MS-SSIM in image compression and denoising applications.
Comments48 pages, 10 figures, 20 tables, Journal of Computational and Applied Mathematics, 118057, 2026
