The main contributions of this work are summarized as follows: 1. A novel image fusion algorithm based on the low-redundancy discrete wavelet frame is proposed.
主要研究成果如下:1.提出了一种新的基于低冗余离散小波框架的图像融合算法。
参考来源 - 基于小波的像素级图像融合算法研究This paper proposes a novel wavelet frame packet analysis approach for automatic retrieval of images in a colored natural image database.
针对彩色自然图像数据库,提出了一种新的基于小波帧包变换的自动检索方法。
参考来源 - 基于小波变换的彩色自然图像数据库自动检索This paper is composed of three parts and mainly explores the perturbation of wavelet frame.
本文共分三部分。 其中着重研究了小波框架的摄动这一方面。
参考来源 - 小波框架·2,447,543篇论文数据，部分数据来源于NoteExpress
Based on the single scaling wavelet frame theory and radial basis function neural network, a multi dimensional input and output wavelet network is constructed.
在探索单尺度径向小波框架与径向基函数网络对函数逼近特性相似的基础上，构造了单尺度径向基小波网络。
The method adopts redundant wavelet, which is called wavelet frame, to decompose and then to achieve the characteristic description of stability and translational constancy.
本文提出一种新的多分辨率纹理分类方法，该方法采用称为小波帧的冗余小波分解，从而获得具有稳定性和平移不变性的特征描述。
Wavelet frame redundancy leads to robustness, that wavelet transform coefficient obtained in low accuracy can be used to reconstruct original signal in comparative high accuracy.
小波框架的冗余可导致鲁棒性，冗余使得低精度下获得的小波系数能在相对高的精度下重建原始信号。
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