Image Compression A collection of simple routines for image compression using different techniques. 圖象壓縮的不同方法 BTCODE: Image compression Using Block Truncation Coding. PYRAMID: Image compression based on Gaussian Pyramids. DCTCOMPR: Image compression based on Discrete Cosine Transform. IMCOMPR: Image compression based on Singular Value Decomposition. The given codes can be also used in 2D noise suppression. Notes: The function "conv2fft" performs a 2D FFT-based convolution. Type "help conv2fft" on Matlab command window for more informations.
標(biāo)簽： Compression compression collection different
上傳時間： 2016-05-11
上傳用戶：磊子226
離散傅里葉變換是20世紀(jì)60年代是計算復(fù)雜性研究的主要里程碑之一，1965年Cooley和Tukey所研究的計算離散傅里葉變換(Discrete Fourier Test)的快速傅氏變換(FFT)將計算量從О(n2)下降至О(nlogn)，推進(jìn)了FFT更深層、更廣法的研究與應(yīng)用。 這是一個傅氏變換的MPI程序，用C語言實(shí)現(xiàn)。
上傳時間： 2013-12-16
上傳用戶：luke5347
1D FDTD simulation between free space and dielectric medium with absorbing boundary conditions,calculating the fourier transform of electric field
標(biāo)簽： simulation dielectric conditions absorbing
上傳時間： 2016-09-24
上傳用戶：gundamwzc
經(jīng)典分?jǐn)?shù)階傅立葉變換，土耳其算法（The fast Fractional Fourier Transform）
標(biāo)簽： 分?jǐn)?shù)階 傅立葉變換
上傳時間： 2014-01-21
上傳用戶：李彥東
16點(diǎn)FFT VHDL源程序，The xFFT16 fast Fourier transform (FFT) Core computes a 16-point complex FFT. The input data is a vector of 16 complex values represented as 16-bit 2’s complement numbers – 16-bits for each of the real and imaginary component of a datum.
上傳時間： 2013-12-20
上傳用戶：yph853211
This program produces a Frequency Domain display from the Time Domain * data input using the Fast Fourier Transform.
標(biāo)簽： Domain Frequency the produces
上傳時間： 2013-12-11
上傳用戶：mpquest
FRFT時頻變換代碼（參考算法：H.M. Ozaktas, M.A. Kutay, and G. Bozdagi.Digital computation of the fractional Fourier transform.IEEE Trans. Sig. Proc., 44:2141--2150, 1996.）
標(biāo)簽： H.M. G. M.A. computation
上傳時間： 2013-12-21
上傳用戶：希醬大魔王
-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.
標(biāo)簽： present modalities existence different
上傳時間： 2014-03-04
上傳用戶：15736969615
The frequency domain plays an important role in image processing to smooth, enhance, and detect edges of images. Although image data typically does not include imaginary values, the fast Fourier transform (FFT) has been used for obtaining spectra. In this paper, the fast Hartley transform (FHT) is used to transform two-dimensional image data. Because the Hartley transform is real valued, it does not require complex operations. Both spectra and autocorrelations of two-dimensional ultrasound images of normal and abnormal livers were computed.
標(biāo)簽： processing frequency important enhance
上傳時間： 2014-01-08
上傳用戶：1051290259
An important book that cover wavelet domain. It help a lot in understanding wavelets and their applications. Beside explanations of the transform and application of this, there are also presents the reasons why wavelets are better than Fourier transform.
標(biāo)簽： understanding important wavelets wavelet
上傳時間： 2017-06-14
上傳用戶：lanhuaying
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