Description: 关于二维小波变换的程序 [精华]
说明：此算法重在概念，速度并不是很快。因为FOR循环的缘故。此程序从循环矩阵的观点出发，把圆周卷积和快速幅里叶变换建立了联系。实现了分解和无失真重构。它只做了一层分解，即将256x256图形分解成为64x64的四个图形，避免了使用WKEEP()的困惑。主要思想为用小波滤波器族构造正交阵W,变换写为B=W*A*W ,反变换为：A=W *A*W,这与所有正交变换无异。W为循环正交矩阵，因此可用FFT实现快速运算，难点就在重构矩阵上。若用矩阵概念明确，一个共扼转置可以搞顶。但FFT的使用必须找到与分解序列的关系。-on Wavelet Transform procedures [best] Note : This algorithm important concept is not speed quickly. Because FOR cycle nowadays. This program cycle from the point of view of Matrix, circular convolution and fast Fourier transform is established links. To achieve the decomposition and reconstruction without distortion. It only had one decomposition, 256x256 graphics will be 64x64 decomposition of the four graphics, avoiding the use of WKEEP () confusion. The main idea of using wavelet filter generator orthogonal array W, write to transform B = W * A * W, anti-Transform : A = W * A * W, which is orthogonal transformation all the same. W cycle orthogonal matrix, can be used to achieve rapid FFT computation, on the difficult reconstruction matrix. If a clear matrix concept, a home to a total o Platform: |
Size: 3785 |
Author:罗松溪 |
Hits:
Description: 关于二维小波变换的程序 [精华]
说明：此算法重在概念，速度并不是很快。因为FOR循环的缘故。此程序从循环矩阵的观点出发，把圆周卷积和快速幅里叶变换建立了联系。实现了分解和无失真重构。它只做了一层分解，即将256x256图形分解成为64x64的四个图形，避免了使用WKEEP()的困惑。主要思想为用小波滤波器族构造正交阵W,变换写为B=W*A*W ,反变换为：A=W *A*W,这与所有正交变换无异。W为循环正交矩阵，因此可用FFT实现快速运算，难点就在重构矩阵上。若用矩阵概念明确，一个共扼转置可以搞顶。但FFT的使用必须找到与分解序列的关系。-on Wavelet Transform procedures [best] Note : This algorithm important concept is not speed quickly. Because FOR cycle nowadays. This program cycle from the point of view of Matrix, circular convolution and fast Fourier transform is established links. To achieve the decomposition and reconstruction without distortion. It only had one decomposition, 256x256 graphics will be 64x64 decomposition of the four graphics, avoiding the use of WKEEP () confusion. The main idea of using wavelet filter generator orthogonal array W, write to transform B = W* A* W, anti-Transform : A = W* A* W, which is orthogonal transformation all the same. W cycle orthogonal matrix, can be used to achieve rapid FFT computation, on the difficult reconstruction matrix. If a clear matrix concept, a home to a total o Platform: |
Size: 3072 |
Author:罗松溪 |
Hits:
Description: This routine performs a 1-D Periodic Orthogonal Wavelet decomposition. It also performs the row decomposition component of a 2-D wavelet transform. An input signal x[n] is low pass and high pass filtered and the resulting signals decimated by factor of two. This results in a reference signal r1[n] which is the decimated output obtained by dropping the odd samples of the low pass filter output and a detail signal d[n] obtained by dropping the odd samples of the highpass filter output. A circular convolution algorithm is implemented and hence the wavelet transform is periodic. The reference signal and the detail signal are each half the size of the original signal. Platform: |
Size: 1024 |
Author:pranav |
Hits:
Description: Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。-Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. Aiming at the problem, an approach is presented for rotation invariant medical texture classification based on Gabor wavelet transform. Rotation normalization is achieved by circular shift of the feature elements, so that all images have the same dominant direction. Experimental result shows that Gabor wavelet transform with circular operator of rotation normalization has well precision to classify the medical CT image. Platform: |
Size: 407552 |
Author:li |
Hits:
Description: 128点的小波变换，小波函数采用db3，变换过程采用FFT圆周卷积快速实现卷积过程。-128-point wavelet transform, wavelet function using db3 conversion process using the FFT circular convolution convolution process quickly. Platform: |
Size: 3072 |
Author:yangdong |
Hits:
Description: These are the files to calculate the Circular Shift and Conversion using Double Discrete wavelet transform. Platform: |
Size: 2048 |
Author:Pasha |
Hits:
Description: E ective image compression requires a non-expansive discrete wavelet transform (DWT) be
employed consequently, image border extension is a critical issue. Ideally, the image border
extension method should not introduce distortion under compression. It has been shown in
literature that symmetric extension performs better than periodic extension. However, the
non-expansive, symmetric extension using fast Fourier transform and circular convolution
DWT methods require symmetric lters. This precludes orthogonal wavelets for image compression
since they cannot simultaneously possess the desirable properties of orthogonality
and symmetry. Thus, biorthogonal wavelets have been the de facto standard for image compression
applications. The viability of symmetric extension with biorthogonal wavelets is
the primary reason cited for their superior performance.-E ective image compression requires a non-expansive discrete wavelet transform (DWT) be
employed consequently, image border extension is a critical issue. Ideally, the image border
extension method should not introduce distortion under compression. It has been shown in
literature that symmetric extension performs better than periodic extension. However, the
non-expansive, symmetric extension using fast Fourier transform and circular convolution
DWT methods require symmetric lters. This precludes orthogonal wavelets for image compression
since they cannot simultaneously possess the desirable properties of orthogonality
and symmetry. Thus, biorthogonal wavelets have been the de facto standard for image compression
applications. The viability of symmetric extension with biorthogonal wavelets is
the primary reason cited for their superior performance. Platform: |
Size: 2140160 |
Author:bamerni |
Hits:
Search - circular wavelet