“function digitalHolographyGUI % 改进后的数字全息成像仿真系统（包含完整执行按钮和修正后的指标计算） f = figure('Name','数字全息成像仿真系统 V2','NumberTitle','off',... 'Units','normalized','Position',[0.1,0.1,0.95,0.8],... 'MenuBar','none','ToolBar','none'); % 界面布局优化 leftPanel = uipanel('Parent',f,'Units','normalized','Position',[0,0,0.65,1],... 'BorderType','etchedin'); rightPanel = uipanel('Parent',f,'Units','normalized','Position',[0.67,0.1,0.3,0.9],... 'Title','处理流程控制','FontSize',11); % 图像显示区域增强 axPositions = [ 0.02 0.55 0.30 0.40 0.34 0.55 0.30 0.40 0.66 0.55 0.30 0.40 0.02 0.05 0.30 0.40 0.34 0.05 0.30 0.40 0.66 0.05 0.30 0.40]; titles = {'全息图','傅里叶谱','重建像（未滤波）','滤波后傅里叶谱','重建像（Lee滤波）','原始物体'}; data.axes = gobjects(6,1); for i = 1:6 data.axes(i) = axes('Parent',leftPanel,'Units','normalized',... 'Position',axPositions(i,:),'XTick',[],'YTick',[]); title(data.axes(i), titles{i},'FontSize',9) end % 增强型控制按钮（带状态指示） btnProps = {'Style','pushbutton','Units','normalized','FontSize',10}; btnPositions = [ 0.1 0.85 0.8 0.08 0.1 0.75 0.8 0.08 0.1 0.65 0.8 0.08 0.1 0.55 0.8 0.08 0.1 0.45 0.8 0.08 0.1 0.35 0.8 0.08]; btnLabels = {'加载目标图像','仿真全息图','窗口傅里叶滤波','重构图像','Lee滤波处理','计算指标'}; callbacks = {@loadObject, @simulateHologram, @applyWFFilter,... @reconstructImage, @applyLeeFilter, @calculateMetrics}; for i = 1:6 uicontrol(rightPanel, btnProps{:},... 'Position',btnPositions(i,:),... 'String',btnLabels{i},... 'Callback',callbacks{i},... 'BackgroundColor',[0.94 0.94 0.94]); end % 指标显示区域增强 data.metricsText = uicontrol('Parent',rightPanel,'Style','text',... 'Units','normalized','Position',[0.05 0.05 0.9 0.25],... 'FontSize',9,'HorizontalAlignment','left',... 'String','指标结果待计算...','BackgroundColor','w'); % 数据存储结构优化 data.object = []; data.hologram = []; data.F_filtered = []; data.recon_noLee = []; data.recon_Lee = []; guidata(f,data); %% 回调函数改进 % 每个回调函数添加了参数验证和错误处理 function loadObject(~,~) [file,path] = uigetfile({'*.png;*.jpg;*.tif;*.bmp','图像文件'}); if isequal(file,0), return; end data = guidata(f); img = im2double(imread(fullfile(path,file))); if size(img,3)==3, img = rgb2gray(img); end data.object = imresize(img,[512 512]); % 提高分辨率 axes(data.axes(6)); imshow(data.object,[]); title(data.axes(6),'原始物体 (512×512)'); guidata(f,data); end function simulateHologram(~,~) data = guidata(f); if isempty(data.object) warndlg('请先加载目标图像!','警告','modal'); return; end obj = padarray(data.object,[128 128],'both'); % 增加零填充 [M,N] = size(obj); fx = 80; fy = 80; % 优化离轴频率 [x,y] = meshgrid(1:N,1:M); phase = 2*pi*rand(M,N); O = obj .* exp(1i*phase); R = exp(1i*2*pi*(fx*x/N + fy*y/M)); H = abs(O + R).^2; data.hologram = H(129:end-128,129:end-128); % 裁剪回原尺寸 axes(data.axes(1)); imshow(data.hologram,[]); % 优化傅里叶显示 F = log(fftshift(abs(fft2(data.hologram)))+1); axes(data.axes(2)); imshow(F,[]); guidata(f,data); end function applyWFFilter(~,~) data = guidata(f); if isempty(data.hologram) warndlg('请先生成全息图!','警告','modal'); return; end H = data.hologram; F = fftshift(fft2(H)); [M,N] = size(F); % 自动侧带检测 [~,maxIdx] = max(F(:)); [cy,cx] = ind2sub(size(F),maxIdx); mask = zeros(M,N); radius = 50; [X,Y] = meshgrid(1:N,1:M); mask((X-cx).^2 + (Y-cy).^2 <= radius^2) = 1; data.F_filtered = F .* mask; axes(data.axes(4)); imshow(log(abs(data.F_filtered)+1),[]); guidata(f,data); end function reconstructImage(~,~) data = guidata(f); if isempty(data.F_filtered) warndlg('请先进行傅里叶滤波!','警告','modal'); return; end rec = abs(ifft2(ifftshift(data.F_filtered))); data.recon_noLee = imresize(rec,size(data.object)); % 尺寸匹配 axes(data.axes(3)); imshow(data.recon_noLee,[]); guidata(f,data); end function applyLeeFilter(~,~) data = guidata(f); if isempty(data.recon_noLee) warndlg('请先重构图像!','警告','modal'); return; end % 改进的Lee滤波（自适应窗口） I = data.recon_noLee; windowSize = 7; % 增大窗口尺寸 I = (I - min(I(:))) / (max(I(:)) - min(I(:))); % 归一化 data.recon_Lee = leeFilter(I, windowSize); axes(data.axes(5)); imshow(data.recon_Lee,[]); guidata(f,data); end function calculateMetrics(~,~) data = guidata(f); if isempty(data.recon_Lee) warndlg('请先完成全部处理流程!','警告','modal'); return; end orig = data.object; rec1 = data.recon_noLee; rec2 = data.recon_Lee; % 指标计算优化 [C1, SI1, PSNR1, ENL1] = computeMetrics(orig, rec1); [C2, SI2, PSNR2, ENL2] = computeMetrics(orig, rec2); metricStr = {... '【未滤波】',... sprintf('对比度 C: %.3f',C1),... sprintf('散斑指数 SI: %.3f',SI1),... sprintf('PSNR: %.2f dB',PSNR1),... sprintf('ENL: %.1f',ENL1),... '【Lee滤波】',... sprintf('对比度 C: %.3f',C2),... sprintf('散斑指数 SI: %.3f',SI2),... sprintf('PSNR: %.2f dB',PSNR2),... sprintf('ENL: %.1f',ENL2)}; set(data.metricsText,'String',metricStr); end function [C, SI, PSNR_val, ENL] = computeMetrics(ref, rec) ref = im2double(ref); rec = (rec - min(rec(:))) / (max(rec(:)) - min(rec(:))); % 归一化 % 对比度计算 C = std(rec(:)) / (mean(rec(:)) + eps); % 散斑指数 SI = var(rec(:)) / (mean(rec(:))^2 + eps); % 改进的PSNR计算 maxVal = 1; % 基于归一化后的值 mse = mean((ref(:) - rec(:)).^2); PSNR_val = 10*log10(maxVal^2/(mse + eps)); % 添加eps防止除零 % 等效视数 ENL = (mean(rec(:))^2) / (var(rec(:)) + eps); end function I_out = leeFilter(I, windowSize) % 增强型Lee滤波 I = im2double(I); padSize = floor(windowSize/2); I_pad = padarray(I,[padSize padSize],'symmetric'); I_out = zeros(size(I)); for i = 1:size(I,1) for j = 1:size(I,2) window = I_pad(i:i+windowSize-1, j:j+windowSize-1); mu = mean(window(:)); sigma2 = var(window(:)); K = max(0, 1 - (mean(I(:))^2) / (sigma2 + eps)); I_out(i,j) = mu + K*(window(padSize+1,padSize+1) - mu); end end end end”散斑对比度和散斑指数这两各指标不是降低才能体现散斑噪声得到抑制吗，为什么运行后，这两个指标反而提升了，给出修改后的完整代码