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基于MATLAB的噪声图像处理方案

news 2025/11/13 5:51:28

基于MATLAB的噪声图像处理方案，包含噪声方差估计、Gamma校正和白平衡的实现代码及原理详解：

一、系统架构设计

请添加图片描述

二、核心算法实现

1. 噪声方差估计（改进中值滤波法）

function sigma2 = estimate_noise_var(img, block_size=8, texture_threshold=0.1)% 分块计算局部方差[h,w] = size(img);variances = zeros(floor(h/block_size), floor(w/block_size));for i = 1:block_size:hfor j = 1:block_size:w% 提取局部块patch = img(max(1,i-block_size/2):min(h,i+block_size/2), ...max(1,j-block_size/2):min(w,j+block_size/2));local_var = var(patch(:));% 纹理强度计算grad_x = imfilter(patch, fspecial('sobel')/8, 'replicate');grad_y = imfilter(patch, fspecial('sobel')'/8, 'replicate');texture = mean(grad_x(:).^2 + grad_y(:).^2);% 阈值筛选if texture < texture_thresholdvariances(ceil(i/block_size), ceil(j/block_size)) = local_var;endendend% 中值滤波估计全局噪声sigma2 = median(variances(:)) * 2; % 考虑泊松噪声成分
end

2. Gamma校正（自适应查找表法）

function img_gamma = adaptive_gamma_correction(img, target_contrast=0.35)% 直方图均衡化预处理img_eq = imadjust(img);% Gamma值自适应计算[counts, binLocations] = imhist(img_eq);cdf = cumsum(counts)/numel(img_eq);% 寻找目标对比度对应的Gamma值[~, idx] = min(abs(cdf - target_contrast));optimal_gamma = binLocations(idx)/255;% 构建Gamma校正LUTlut = zeros(256,1);for i = 1:256lut(i) = 255 * (i/255)^optimal_gamma;end% 应用校正img_gamma = imadjust(img, lut, []);
end

3. 白平衡算法（双模式实现）

function balanced_img = white_balance(img, method='grayworld')img = im2double(img);switch methodcase 'grayworld'% 灰度世界算法avg_r = mean(img(:,:,1)(:));avg_g = mean(img(:,:,2)(:));avg_b = mean(img(:,:,3)(:));scale = avg_g / [avg_r, 1, avg_b];balanced_img = bsxfun(@times, img, scale);case 'whitepatch'% 白点估计算法[rows, cols, ~] = size(img);sum_rgb = sum(img, 3);threshold = prctile(sum_rgb(:), 95);white_pixels = sum_rgb > threshold;avg_r = mean(img(:,:,1)(white_pixels));avg_g = mean(img(:,:,2)(white_pixels));avg_b = mean(img(:,:,3)(white_pixels));scale = max([avg_r, avg_g, avg_b]) / [avg_r, avg_g, avg_b];balanced_img = bsxfun(@times, img, scale);otherwiseerror('Unknown method');end% 色彩范围限制balanced_img = max(0, min(1, balanced_img));
end

三、完整处理流程

%% 读取噪声图像
img = imread('noisy_image.jpg');
img = im2double(img);%% 步骤1：噪声方差估计
sigma2 = estimate_noise_var(img);
fprintf('Estimated noise variance: %.4f\n', sigma2);%% 步骤2：Gamma校正
gamma_img = adaptive_gamma_correction(img, 0.4);%% 步骤3：白平衡处理
balanced_img = white_balance(gamma_img, 'whitepatch');%% 结果可视化
figure;
subplot(2,2,1); imshow(img); title('原始图像');
subplot(2,2,2); imshow(gamma_img); title('Gamma校正');
subplot(2,2,3); imshow(balanced_img); title('白平衡处理');
subplot(2,2,4); imshow(img); title('原图对比');%% 量化评估
psnr_before = psnr(img, balanced_img);
ssim_before = ssim(balanced_img, img);
disp(['PSNR: ', num2str(psnr_before), ' dB']);
disp(['SSIM: ', num2str(ssim_before)]);

四、参数说明

参数	作用范围	推荐值	调优方法
`block_size`	噪声估计	8-16	基于图像分辨率动态调整
`texture_threshold`	纹理筛选	0.05-0.2	直方图分析确定最佳阈值
`target_contrast`	Gamma校正	0.3-0.6	峰值信噪比(PSNR)最大化准则
`method`	白平衡	grayworld/whitepatch	场景光照判断