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Description: Matlab实现图像压缩与重构步骤
① 对图像进行小波分解，得到第一层分解的低频系数和高频系数。
② 保留低频系数，对高频系数进行基于神经网络的矢量量化编码，达到压缩。
③ 根据码书以w还原高频系数
④ 根据保留的低频系数和还原的高频系数重构图像
-Matlab realization image compression and the heavy
construction step (1) pair of picture carries on the wavelet to
decompose, obtains the first decomposition the low frequency
coefficient and the high frequency coefficient. (2) Retains the low
frequency coefficient, carries on to the high frequency coefficient
based on the nerve network vector quantification code, achieves the
compression. (3) To w returns to original state the low frequency
coefficient according to the code book and the return to original
state high frequency coefficient heavy composition which the high
frequency coefficient (4) basis retains likes
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Size: 19961 |
Author: 王危结 |
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Description: decompose filter for image in c
Platform: |
Size: 11251 |
Author: wangyg |
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Description: Matlab实现图像压缩与重构步骤
① 对图像进行小波分解，得到第一层分解的低频系数和高频系数。
② 保留低频系数，对高频系数进行基于神经网络的矢量量化编码，达到压缩。
③ 根据码书以w还原高频系数
④ 根据保留的低频系数和还原的高频系数重构图像
-Matlab realization image compression and the heavy
construction step (1) pair of picture carries on the wavelet to
decompose, obtains the first decomposition the low frequency
coefficient and the high frequency coefficient. (2) Retains the low
frequency coefficient, carries on to the high frequency coefficient
based on the nerve network vector quantification code, achieves the
compression. (3) To w returns to original state the low frequency
coefficient according to the code book and the return to original
state high frequency coefficient heavy composition which the high
frequency coefficient (4) basis retains likes
Platform: |
Size: 19456 |
Author: 王危结 |
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Description: decompose filter for image in c
Platform: |
Size: 11264 |
Author: wangyg |
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Description: 首先将一幅图象分解成若干个块，在对各个块进行离散小波分解-First decompose an image into several blocks, each block in the discrete wavelet decomposition
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Size: 1024 |
Author: feng lei |
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Description: 位图分析，可以读入windos下的位图，然后将其分解，笨程序可以用于实现数字图像处理-Bitmap analysis, can be read under windos bitmap, and then decompose, stupid program can be used to realize digital image processing
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Size: 2535424 |
Author: 冯超 |
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Description: curvelet 工具包，功能强大，版本最新，是图像处理，多尺度分析的主要工具。-curvelet toolkits, powerful, version of the latest is the image processing, multi-scale analysis of the major tools.
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Size: 860160 |
Author: 张大山 |
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Description: 基于小波变换的数字水印提取，可以与嵌入程序一起使用，很好的小波应用基础。-wavelet-based watermark pluge out from digtal image，use wavelet decompose.
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Size: 1024 |
Author: tangqinqin |
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Description: 工作在图像特征提取。这里采用多尺度图像分析方法，将轮廓分解投影。-Work in the image feature extraction. Here the multi-scale image analysis method to decompose projection contour.
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Size: 1024 |
Author: 杨树 |
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Description: 本程序可以先分解gif图片的每帧，缩放后再组合一起，达到缩放gif图片的效果-This program can first decompose gif images of each frame, zoom, and then combined together to achieve the effect of scaling gif image
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Size: 332800 |
Author: 劳明 |
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Description: this source code is used to decompose the image into 3 level using discreat wavelet transform. at each level image is decomposed into 4 levels , LL ,LH,HL,HH
LL is the approximation of the image the other are the details of image like edges
Platform: |
Size: 11264 |
Author: afifayounus |
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Description: in this file we save an image as watermark into a gray scale image. here the watermark is embedded into the dc component.first we decompose the image up to 3 levels
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Size: 1388544 |
Author: afifayounus |
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Description: This code does Decompose the RGB Channels of an Image, and then Halftone it using Jarvis technique and then Re-Assemble it as final Color Image.
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Size: 51200 |
Author: Gopi Krishnan S |
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Description: Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(fn,Nsc,Nor,block,noise,parent)
Javier Portilla, Univ. de Granada, 5/02
Last revision: 11/04-Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(fn,Nsc,Nor,block,noise,parent)
Javier Portilla, Univ. de Granada, 5/02
Last revision: 11/04
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Size: 1024 |
Author: ali |
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Description: Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(im,Nsc,Nor,block,noise,parent,covariance,optim,sig)
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using the Full steerable pyramid (2) (High pass residual
splitted into orientations).
JPM, Univ. de Granada, 5/02
Last Revision: 11/04
- Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(im,Nsc,Nor,block,noise,parent,covariance,optim,sig)
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using the Full steerable pyramid (2) (High pass residual
splitted into orientations).
JPM, Univ. de Granada, 5/02
Last Revision: 11/04
Platform: |
Size: 1024 |
Author: ali |
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Description:
Decompose image into subbands, denoise using BLS-GSM method, and recompose again.
fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig)
im: image
Nsc: number of scales
filter: type of filter used (see namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03-
Decompose image into subbands, denoise using BLS-GSM method, and recompose again.
fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig)
im: image
Nsc: number of scales
filter: type of filter used (see namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03
Platform: |
Size: 1024 |
Author: ali |
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Description: Decompose image into subbands (undecimated wavelet), denoise, and recompose again.
fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig)
im : image
Nsc: Number of scales
daub_order: Order of the daubechie fucntion used (must be even).
block: size of neighborhood within each undecimated subband.
noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise)
parent: are we including the coefficient at the central location at the next coarser scale?
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Javier Portilla, Univ. de Granada, 3/03
Revised: 11/04 - Decompose image into subbands (undecimated wavelet), denoise, and recompose again.
fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig)
im : image
Nsc: Number of scales
daub_order: Order of the daubechie fucntion used (must be even).
block: size of neighborhood within each undecimated subband.
noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise)
parent: are we including the coefficient at the central location at the next coarser scale?
covariance: are we considering covariance or just variance?
optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)
sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)
Javier Portilla, Univ. de Granada, 3/03
Revised: 11/04
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Size: 1024 |
Author: ali |
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Description: 应用小波变换对图像进行变换处理：多次压缩及编码-Wavelet transform to transform the image processing: multiple compression and coding
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Size: 3072 |
Author: coco |
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Description: 编写的重构程序中，为了比较分解图像和重构图像，首先绘出经过小波分解的图像，然后再进行重构。-Prepared by the reconstruction program, in order to compare the decompose image and image reconstruction, first drawn by Wavelet Decomposition of an image and then refactor.
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Size: 1024 |
Author: LJ |
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Description: 用于分解图像,检测产品是否合格,此外,它还包括重组图像-Is used to decompose image, detecting the product is qualified, in addition, it also includes restructuring images
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Size: 6144 |
Author: hxyectm |
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