The directional wavelet theory provides a novel sparse representation method for image processing, and it can capture geometrical image structures more efficiently.
方向性小波理论为图像处理提供了一种新的稀疏表示方法，能够更有效地捕捉图像中的几何结构。
Our analysis shows that directional wavelet transforms can better reflect the edge information of images because they better corresponds to the characteristics of image direction and texture.
比较了方向小波变换和传统小波变换在图像边缘检测中的不同之处。分析得出方向小波变换更符合图像的方向、纹理特征，因而更能反映图像的边缘信息。
The merit of both shift invariance and directional selectivity enables the complex wavelet to overcome the artifacts in the discrete orthonormal wavelet denoising.
由于复数小波具备平移不变性和方向选择性等优点，使得它在图像去噪中可以克服离散正交小波变换去噪中存在的毛刺现象。
The merit of both shift invariance and directional selectivity enables the complex wavelet to overcome the artifacts in the discrete orthonormal wavelet denoising.
由于复数小波具备平移不变性和方向选择性等优点，使得它在图像去噪中可以克服离散正交小波变换去噪中存在的毛刺现象。
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