数字图像处理实验(13):PROJECT 05-04,Parametric Wiener Filter
2017-05-27 10:59
176 查看
实验要求:
Objective:
To understand the high performance of the parametric Wiener Filter in image restoration when there are additive noise after the image degradation.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
(a) Implement a blurring filter as in Eq. (5.6-11).
(b) Blur image 5.26(a) in the +45o direction using T = 1, as in Fig. 5.26(b).
(c) Add Gaussian noise of 0 mean and variance of 10 pixels to the blurred image.
(d) Restore the image using the parametric Wiener filter given in Eq. (5.8-3).
本实验属于图像复原技术,使用参数维纳滤波进行图像复原。实验中向图像添加了高斯噪声和运动模糊,最后用参数维纳滤波器复原图像。
实验结果:
上面一行的图像分别是原始图像,模糊后的图像,以及添加高斯噪声后的图像;
下面一行的图像分别是调用维纳滤波器的两种情况,一个是不给参数,默认直接给0,另一个是使用方差计算参数后调用维纳滤波器得到的正确滤波结果。
Objective:
To understand the high performance of the parametric Wiener Filter in image restoration when there are additive noise after the image degradation.
Main requirements:
Ability of programming with C, C++, or Matlab.
Instruction manual:
(a) Implement a blurring filter as in Eq. (5.6-11).
(b) Blur image 5.26(a) in the +45o direction using T = 1, as in Fig. 5.26(b).
(c) Add Gaussian noise of 0 mean and variance of 10 pixels to the blurred image.
(d) Restore the image using the parametric Wiener filter given in Eq. (5.8-3).
本实验属于图像复原技术,使用参数维纳滤波进行图像复原。实验中向图像添加了高斯噪声和运动模糊,最后用参数维纳滤波器复原图像。
% close all; clc; clear all; % 读取图像 img = imread('Fig5.26(a).jpg'); img = im2double(img); figure; subplot(2,3,1); imshow(img); title('original image'); % 模糊图像 PSF = fspecial('motion', 30, 45); img1 = imfilter(img, PSF, 'conv', 'circular'); subplot(2,3,2); imshow(img1); title('filtered image'); % 添加高斯噪声 noise_var = 0.001; img2 = imnoise(img1, 'gaussian', 0, noise_var); subplot(2,3,3); imshow(img2); title('add gaussian noise'); % 参数维纳滤波,NSR直接给0 % Specifying 0 for the NSR is equivalent to creating an ideal inverse filter. % img3 = deconvwnr(img2, PSF, 0.012); img3 = deconvwnr(img2, PSF, 0.0); subplot(2,2,3); imshow(img3); title('Restoration of Blurred, Noisy Image Using NSR = 0'); % 参数维纳滤波,计算方差 % img = double(img); estimated_NSR = noise_var / var(img(:)); img4 = deconvwnr(img2, PSF, estimated_NSR); subplot(2,2,4); imshow(img4); title('Restoration of Blurred, Noisy Image Using Estimated NSR');
实验结果:
上面一行的图像分别是原始图像,模糊后的图像,以及添加高斯噪声后的图像;
下面一行的图像分别是调用维纳滤波器的两种情况,一个是不给参数,默认直接给0,另一个是使用方差计算参数后调用维纳滤波器得到的正确滤波结果。
相关文章推荐
- 数字图像处理实验(13):PROJECT 05-04,Parametric Wiener Filter 标签: 图像处理MATLAB 2017-05-27 10:59
- 数字图像处理实验(12):PROJECT 05-03,Periodic Noise Reduction Using a Notch Filter 标签: 图像处理MATLAB 2017-0
- 数字图像处理实验(9):PROJECT 04-05,Correlation in the Frequency Domain 标签: 图像处理MATLAB 2017-05-25 10:14
- 数字图像处理实验(9):PROJECT 04-05,Correlation in the Frequency Domain
- 数字图像处理实验(11):PROJECT 05-02,Noise Reduction Using a Median Filter
- 数字图像处理实验(5):PROJECT 04-01 [Multiple Uses],Two-Dimensional Fast Fourier Transform
- 数字图像处理实验(8):PROJECT 04-04,Highpass Filtering Using a Lowpass Image
- 数字图像处理实验(12):PROJECT 05-03,Periodic Noise Reduction Using a Notch Filter
- 数字图像处理实验(17):PROJECT 06-04,Color Image Segmentation 标签: 图像处理MATLAB 2017-05-27 21:13
- 数字图像处理实验(6):PROJECT 04-02,Fourier Spectrum and Average Value 标签: 图像处理MATLABfft 2017-05-07 23:1
- 数字图像处理实验(10):PROJECT 05-01 [Multiple Uses],Noise Generators
- 数字图像处理实验(6):PROJECT 04-02,Fourier Spectrum and Average Value
- 数字图像处理实验(11):PROJECT 05-02,Noise Reduction Using a Median Filter 标签: 图像处理MATLAB 2017-05-26 23:
- 数字图像处理实验(7):PROJECT 04-03 , Lowpass Filtering
- 数字图像处理实验(5):PROJECT 04-01 [Multiple Uses],Two-Dimensional Fast Fourier Transform 标签: 图像处理MATLAB数字图像处理
- 数字图像处理实验(7):PROJECT 04-03 , Lowpass Filtering 标签: 图像处理MATLAB 2017-05-25 09:30 109人
- 数字图像处理实验(2):PROJECT 02-02, Reducing the Number of Gray Levels in an Image 标签: 图像处理MATLAB 2017-
- 数字图像处理实验(8):PROJECT 04-04,Highpass Filtering Using a Lowpass Image 标签: 图像处理MATLAB 2017-05-25 0
- 数字图像处理实验(14):PROJECT 06-01,Web-Safe Colors 标签: 图像处理MATLAB 2017-05-27 20:45 116人阅读
- 参数维纳滤波(Parametric Wiener Filter)