實驗要求：

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).

本實驗屬于圖像復原技術，使用參數維納濾波進行圖像復原。實驗中向圖像添加了高斯噪聲和運動模糊，最后用參數維納濾波器復原圖像。

% close all; clc; clear all;% 讀取圖像 img = imread('Fig5.26(a).jpg'); img = im2double(img); figure; subplot(2,3,1); imshow(img); title('original image');% 模糊圖像 PSF = fspecial('motion', 30, 45); img1 = imfilter(img, PSF, 'conv', 'circular'); subplot(2,3,2); imshow(img1); title('filtered image');% 添加高斯噪聲 noise_var = 0.001; img2 = imnoise(img1, 'gaussian', 0, noise_var); subplot(2,3,3); imshow(img2); title('add gaussian noise');% 參數維納濾波，NSR直接給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');% 參數維納濾波，計算方差 % 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');

實驗結果：

上面一行的圖像分別是原始圖像，模糊后的圖像，以及添加高斯噪聲后的圖像；
下面一行的圖像分別是調用維納濾波器的兩種情況，一個是不給參數，默認直接給0，另一個是使用方差計算參數后調用維納濾波器得到的正確濾波結果。

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