再加入VIF指标，注意代码和函数之间的联系
clear,clc,close all
% 加载测试图像
low_light = imread('D:\SVD\实验素材\LOLdataset\eval15\low\547.png');
reference = imread('D:\SVD\实验素材\LOLdataset\eval15\high\547.png'); % 正常光照参考图像
% 初始化结果表
methods = {'HE', 'BIMEF', 'LIME', 'MSRCR', 'HSV-SVD-CLAHE'};
results = zeros(5, 5); % 5种方法 x 5个指标 (PSNR, SSIM, Avg Gradient, Entropy, NIQE)
% 评估每种方法
enhanced_he = he_method(low_light);
[results(1,1), results(1,2), results(1,3), results(1,4), results(1,5)] = evaluate_image(enhanced_he, reference);
enhanced_bimef = bimef(low_light);
[results(2,1), results(2,2), results(2,3), results(2,4), results(2,5)] = evaluate_image(enhanced_bimef, reference);
enhanced_lime = lime_method(low_light);
[results(3,1), results(3,2), results(3,3), results(3,4), results(3,5)] = evaluate_image(enhanced_lime, reference);
enhanced_msrcr = msrcr_method(low_light);
[results(4,1), results(4,2), results(4,3), results(4,4), results(4,5)] = evaluate_image(enhanced_msrcr, reference);
enhanced_proposed = hsv_svd_clahe(low_light);
[results(5,1), results(5,2), results(5,3), results(5,4), results(5,5)] = evaluate_image(enhanced_proposed, reference);
% 显示结果
fprintf('%-25s %8s %8s %12s %10s %10s\n', 'Method', 'PSNR', 'SSIM', 'Avg Gradient', 'Entropy', 'NIQE');
fprintf('-------------------------------------------------------------------------------\n');
for i = 1:5
fprintf('%-25s %8.2f %8.4f %12.4f %10.4f %10.4f\n', ...
methods{i}, results(i,1), results(i,2), results(i,3), results(i,4), results(i,5));
end
% 可视化结果
figure('Position', [100, 50, 1200, 800]);
subplot(2,3,1); imshow(low_light); title('原始低照度图像');
subplot(2,3,2); imshow(enhanced_he); title('HE');
subplot(2,3,3); imshow(enhanced_bimef); title('BIMEF');
subplot(2,3,4); imshow(enhanced_lime); title('LIME');
subplot(2,3,5); imshow(enhanced_msrcr); title('MSRCR');
subplot(2,3,6); imshow(enhanced_proposed); title('HSV-SVD-CLAHE');
% ===================== 改进的本文方法 =====================
% HSV_SVD_CLAHE 低照度图像增强算法
function enhanced_img = hsv_svd_clahe(img, beta, gamma, tau)
% 转换为HSV并计算平均亮度
img_double = im2double(img);
hsv_img = rgb2hsv(img_double);
V = hsv_img(:,:,3);
mean_brightness = mean(V(:));
% 参数自适应调整
if nargin < 2 || isempty(beta)
if mean_brightness < 0.2 % 极暗图像
beta = 3.2;
gamma = 0.6;
tau = 0.999; % 高阈值避免块状伪影
disp('参数模式: 极暗图像 (beta=3.2, gamma=0.6, tau=0.999)');
elseif mean_brightness < 0.5 % 低照度图像
beta = 2.5;
gamma = 0.7;
tau = 0.99; % 中等阈值
disp('参数模式: 低照度图像 (beta=2.5, gamma=0.7, tau=0.99)');
else % 正常图像
beta = 1.6;
gamma = 0.85;
tau = 0.95; % 较低阈值保留细节
disp('参数模式: 正常图像 (beta=1.6, gamma=0.85, tau=0.95)');
end
end
if nargin < 3 || isempty(gamma), gamma = 0.8; end
if nargin < 4 || isempty(tau), tau = 0.999; end % 默认高阈值
% 奇异值分解
[U, S, V_svd] = svd(V, 'econ');
sigma = diag(S);
total_energy = sum(sigma.^2);
% 确定主次成分分界点
cum_energy_ratio = cumsum(sigma.^2) / total_energy;
idx = find(cum_energy_ratio >= tau, 1);
if isempty(idx), idx = numel(sigma); end
% 主次成分差异化处理
sigma_enhanced = zeros(size(sigma));
sigma_enhanced(1:idx) = sigma(1:idx) * beta; % 主成分增强
sigma_enhanced(idx+1:end) = sigma(idx+1:end) * gamma; % 次成分抑制
% 重构亮度通道
V_enhanced = U * diag(sigma_enhanced) * V_svd';
V_enhanced = min(max(V_enhanced, 0), 1);
% 重组HSV并转RGB
hsv_enhanced = hsv_img;
hsv_enhanced(:,:,3) = V_enhanced;
rgb_enhanced = hsv2rgb(hsv_enhanced);
% CLAHE增强 (Lab空间)
lab_img = rgb2lab(rgb_enhanced);
L = lab_img(:,:,1) / 100;
L_clahe = adapthisteq(L, 'ClipLimit', 0.01, 'NumTiles', [8,8]);
lab_img(:,:,1) = L_clahe * 100;
enhanced_img = lab2rgb(lab_img, 'OutputType', 'double');
% 小波去噪（仅低照度图像）
if mean_brightness < 0.5
try
enhanced_img = wavelet_denoise(enhanced_img);
disp('应用小波去噪');
catch ME
warning('去噪步骤跳过: %s', ME.message);
end
end
% 转换为uint8
enhanced_img = im2uint8(enhanced_img);
end
% ===================== 修正的小波去噪函数 =====================
function denoised_img = wavelet_denoise(img)
% 小波去噪处理泊松噪声
% 使用参数：
% Wavelet: sym8 (适合图像去噪)
% Level: 3 (中等分解层次)
% ThresholdRule: 'median' (修正为有效参数)
% DenoisingMethod: 'Bayes' (贝叶斯去噪)
% 初始化输出
denoised_img = zeros(size(img));
% 小波去噪参数
wavelet = 'sym8';
level = 3;
% 对每个通道分别处理
for ch = 1:3
channel = img(:,:,ch);
% 修正的阈值规则 - 使用有效的'median'选项
denoised_channel = wdenoise2(channel, level, ...
'Wavelet', wavelet, ...
'DenoisingMethod', 'Bayes', ...
'ThresholdRule', 'median', ... % 修正为有效参数
'NoiseEstimate', 'LevelDependent');
denoised_img(:,:,ch) = denoised_channel;
end
end
% ===================== 评估函数 =====================
function [psnr_val, ssim_val, avg_grad, entropy_val, niqe_val] = evaluate_image(enhanced, reference)
% 输入验证
if nargin < 2, reference = []; end
% PSNR计算
if ~isempty(reference) && isequal(size(enhanced), size(reference))
psnr_val = psnr(enhanced, reference);
ssim_val = ssim(enhanced, reference);
else
psnr_val = NaN;
ssim_val = NaN;
end
% 平均梯度
gray_img = rgb2gray(im2double(enhanced));
[Gx, Gy] = imgradientxy(gray_img,"sobel");
avg_grad = mean(sqrt(Gx.^2 + Gy.^2), 'all');
% 平均梯度范围 图像质量判断 可能原因
% < 0.05 质量差（模糊/低对比） 严重模糊、失焦、低对比度场景
% 0.05 ~ 0.15 质量中等 轻度模糊、纹理简单
% 0.15 ~ 0.3 质量良好 清晰、细节丰富
% > 0.3 质量优（或噪声干扰） 高锐度/高噪声
% 信息熵
entropy_val = entropy(gray_img);
% NIQE指标
niqe_val = niqe(enhanced);
end
% ===================== 其他方法保持不变 =====================
% 直方图均衡化 (HE)
function enhanced = he_method(img)
enhanced = histeq(img);
end
% BIMEF方法
function enhanced = bimef(img)
% 读取彩色图像
img_double = im2double(img);
gray_img = rgb2gray(img_double);
log_img = log(1 + gray_img);
% BIMEF 算法参数
filter_size = 5;
step = 1;
enhanced_img = zeros(size(log_img));
for i = step:step:size(log_img, 1)-filter_size+1
for j = step:step:size(log_img, 2)-filter_size+1
h = ones(filter_size, filter_size) / (filter_size^2);
enhanced_img(i:i+filter_size-1, j:j+filter_size-1) = ...
exp(h * log_img(i:i+filter_size-1, j:j+filter_size-1));
end
end
enhanced_img = (enhanced_img - min(enhanced_img(:))) / ...
(max(enhanced_img(:)) - min(enhanced_img(:)));
enhanced_img = uint8(255 * enhanced_img);
% 转换为彩色
hsv_img = rgb2hsv(img_double);
hsv_img(:, :, 3) = mat2gray(enhanced_img);
enhanced = hsv2rgb(hsv_img);
enhanced = im2uint8(enhanced);
end
% LIME方法
function enhanced = lime_method(img)
img = im2double(img);
T = max(img, [], 3);
r = 16; eps = 0.01;
L = imguidedfilter(T, img, 'NeighborhoodSize', [r r], 'DegreeOfSmoothing', eps);
L = min(L, 1);
L_adjusted = imadjust(L, [min(L(:)) max(L(:))], [0.2 1]);
enhanced = zeros(size(img));
for ch = 1:3
enhanced(:,:,ch) = img(:,:,ch) ./ max(L_adjusted, 0.01);
end
enhanced = min(max(enhanced, 0), 1);
enhanced = im2uint8(enhanced);
end
% MSRCR方法
function enhanced_img = msrcr_method(img)
scales = [15, 80, 250];
alpha = 125; beta = 46; G = 5;
img = im2double(img);
msr = zeros(size(img));
for i = 1:numel(scales)
sigma = scales(i);
gaus_filter = fspecial('gaussian', 2*ceil(3*sigma)+1, sigma);
for ch = 1:3
log_img = log(img(:,:,ch) + 1e-6);
blurred = imfilter(img(:,:,ch), gaus_filter, 'symmetric');
log_blurred = log(blurred + 1e-6);
msr(:,:,ch) = msr(:,:,ch) + (log_img - log_blurred)/numel(scales);
end
end
color_restored = zeros(size(msr));
for ch = 1:3
I_ch = img(:,:,ch);
col_sum = sum(img,3);
color_restored(:,:,ch) = beta * (log(alpha*I_ch + 1) - log(col_sum + 1));
end
enhanced_img = G * (0.5*msr + 0.5*color_restored);
enhanced_img = (enhanced_img - min(enhanced_img(:))) / ...
(max(enhanced_img(:)) - min(enhanced_img(:)));
enhanced_img = im2uint8(enhanced_img);
end
本文介绍了使用Matlab进行图像处理的基础操作，包括读取图片、转换颜色空间、获取图片尺寸等基本步骤。同时，还涉及了如何利用Matlab创建零矩阵以备后续处理之用。
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