理論見博客：

http://www.cnblogs.com/changkaizhao/p/3266798.html

http://www.cnblogs.com/Imageshop/p/3281703.html

由于課程作業的需要，僅僅進行簡單研究。

1.主函數

clc; clear all; close all; J = imread('4.jpg'); J = double(J); J = J ./255 ; figure(1); imshow(J); %% 求暗通道圖像 Jdark = min(min()); Jdark = Idark(J); figure(2);imshow(Jdark,[]);%% % 注意：何凱明使用了soft matting方法對得到的粗透射率Jt進行細化 % 本代碼采用梯度導向濾波實現 Jdark = gradient_guidedfilter(Jdark,Jdark, 0.04); figure(3);imshow(Jdark,[]); %% 大氣物理模型 J = I*t + A*(1-t) 【直接衰減項】+【大氣光照】 % 透射率 t與深度的關系 t=exp(-a*depth) w = 0.95; %霧的保留系數 Jt = 1 - w*Jdark; %求解透射率%% 求解全局大氣光照 % 1.首先對輸入的有霧圖像I求解其暗通道圖像Jdark。 % 2.選擇Jdark總像素點個數千分之一（N/1000）個最亮的像素點，記錄像素點（x,y）坐標 % 3.根據點的坐標分別在原圖像J的三個通道（r,g,b）內找到這些像素點并加和得到（sum_r,sum_g,sum_b）. % 4.Ac=[Ar,Ag,Ab]. 其中Ar=sum_r/N; Ag=sum_g/N; Ab=sum_b/N. [m,n,~] = size(J); N = floor( m*n./1000 ); MaxPos = [0,0]; % 初始化 for i=1:1:NMaxValue = max(max(Jdark));[x,y] = find(Jdark==MaxValue);Jdack(Jdark==MaxValue) = 0; %最大值置零，尋找下一次次大值%檢查長度MaxPos = vertcat(MaxPos,[x,y]);Cnt = length(MaxPos(1));if Cnt > Nbreak;end end MaxPosN = MaxPos(2:N+1,:);Rsum = 0; Jr = J(:,:,1); Gsum = 0; Jg = J(:,:,2); Bsum = 0; Jb = J(:,:,3); for j=1:1:NRsum = Rsum + Jr(MaxPosN(j,1),MaxPosN(j,2));Gsum = Gsum + Jg(MaxPosN(j,1),MaxPosN(j,2));Bsum = Bsum + Jb(MaxPosN(j,1),MaxPosN(j,2)); endAc = [Rsum/N, Gsum/N, Bsum/N];%% 求解清晰的圖像 % 根據 J = I*t + A*(1-t) I = (J-A)/Jt + A Iorg = zeros(m,n,3); for i = 1:1:mfor j = 1:1:nfor k = 1:1:3Iorg(i,j,k) = (J(i,j,k)-Ac(k)) ./ Jt(i,j) + Ac(k);endend end figure(4); imshow(Iorg,[]);

2.暗通道函數

function Jdark = Idark( I ) % output： Jdark = min(min(r),min(g),min(b)); Wnd = 3; Ir = I(:,:,1); Ig = I(:,:,2); Ib = I(:,:,3); %% 圖像拓展 [m,n,~] = size(I); Irr = zeros(m+Wnd-1, n+Wnd-1); Irr((Wnd-1)/2 : m+(Wnd-1)/2-1 , (Wnd-1)/2 : n+(Wnd-1)/2-1 ) = Ir; Igg = zeros(m+Wnd-1, n+Wnd-1); Igg((Wnd-1)/2 : m+(Wnd-1)/2-1 , (Wnd-1)/2 : n+(Wnd-1)/2-1 ) = Ig; Ibb = zeros(m+Wnd-1, n+Wnd-1); Ibb((Wnd-1)/2 : m+(Wnd-1)/2-1, (Wnd-1)/2 : n+(Wnd-1)/2-1 ) = Ib; %% 暗通道 for i=1:1:mfor j=1:1:nRmin = min(min ( Irr(i:i+Wnd-1, j:j+Wnd-1) ));Gmin = min(min ( Igg(i:i+Wnd-1, j:j+Wnd-1) ));Bmin = min(min ( Ibb(i:i+Wnd-1, j:j+Wnd-1) ));Jdark(i,j) = min(min(Rmin,Gmin),Bmin);end endend

3.基于梯度的指導濾波器

function q = gradient_guidedfilter(I, p, eps) % GUIDEDFILTER O(1) time implementation of guided filter. % % - guidance image: I (should be a gray-scale/single channel image) % - filtering input image: p (should be a gray-scale/single channel image) % - regularization parameter: eps r=16; [hei, wid] = size(I); N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels. mean_I = boxfilter(I, r) ./ N; mean_p = boxfilter(p, r) ./ N; mean_Ip = boxfilter(I.*p, r) ./ N; cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch. mean_II = boxfilter(I.*I, r) ./ N; var_I = mean_II - mean_I .* mean_I; %weight epsilon=(0.001*(max(p(:))-min(p(:))))^2; r1=1; N1 = boxfilter(ones(hei, wid), r1); % the size of each local patch; N=(2r+1)^2 except for boundary pixels. mean_I1 = boxfilter(I, r1) ./ N1; mean_II1 = boxfilter(I.*I, r1) ./ N1; var_I1 = mean_II1 - mean_I1 .* mean_I1; chi_I=sqrt(abs(var_I1.*var_I)); weight=(chi_I+epsilon)/(mean(chi_I(:))+epsilon); gamma = (4/(mean(chi_I(:))-min(chi_I(:))))*(chi_I-mean(chi_I(:))); gamma = 1 - 1./(1 + exp(gamma)); %result a = (cov_Ip + (eps./weight).*gamma) ./ (var_I + (eps./weight)); b = mean_p - a .* mean_I; mean_a = boxfilter(a, r) ./ N; mean_b = boxfilter(b, r) ./ N; q = mean_a .* I + mean_b; end
function imDst = boxfilter(imSrc, r) % BOXFILTER O(1) time box filtering using cumulative sum % % - Definition imDst(x, y)=sum(sum(imSrc(x-r:x+r,y-r:y+r))); % - Running time independent of r; % - Equivalent to the function: colfilt(imSrc, [2*r+1, 2*r+1], 'sliding', @sum); % - But much faster. [hei, wid] = size(imSrc); imDst = zeros(size(imSrc)); %cumulative sum over Y axis imCum = cumsum(imSrc, 1); %difference over Y axis imDst(1:r+1, :) = imCum(1+r:2*r+1, :); imDst(r+2:hei-r, :) = imCum(2*r+2:hei, :) - imCum(1:hei-2*r-1, :); imDst(hei-r+1:hei, :) = repmat(imCum(hei, :), [r, 1]) - imCum(hei-2*r:hei-r-1, :); %cumulative sum over X axis imCum = cumsum(imDst, 2); %difference over X axis imDst(:, 1:r+1) = imCum(:, 1+r:2*r+1); imDst(:, r+2:wid-r) = imCum(:, 2*r+2:wid) - imCum(:, 1:wid-2*r-1); imDst(:, wid-r+1:wid) = repmat(imCum(:, wid), [1, r]) - imCum(:, wid-2*r:wid-r-1); end

4.輸出結果

5.參考文獻

[1] He K, Sun J, Tang X. Single image haze removal using dark channel prior[C]// Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009:1956-1963.
[2] Kou F, Chen W, Wen C, et al. Gradient Domain Guided Image Filtering.[J]. Image Processing IEEE Transactions on, 2015, 24(11):4528-4539.

總結

以上是生活随笔為你收集整理的基于暗通道优先算法的去雾应用Matlab的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。