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我想知道如何从我的 10 位原始数据(它有 rgb-ir 图像数据)数据中提取 rgb 图像?
如何使用 Python 或 MATLAB 阅读?
拍摄时的相机分辨率为 1280x720:室内照片Image for download户外写真Image 2 for download
相机型号:e-CAM40_CUMI4682_MOD
非常感谢
最佳答案
我使用了以下图像处理阶段:
我没有处理 IR 颜色 channel ,而是将其替换为绿色 channel 。
根据你添加的RGB图片,我找到了CFA订单。
CFA(滤色器阵列)顺序为:
B | G
-- --
IR| R
以下 Matlab 代码将图像处理为 RGB:
srcN = 1280;
srcM = 720;
f = fopen('image_raw.raw', 'r');
%Read as transposed matrix dimensions, and transpose the matrix.
%The reason for that, is that Matlab memory oreder is column major, and
%raw image is stored in row major (like C arrays).
I = fread(f, [srcN, srcM], 'uint16');
fclose(f);
I = I';
%Convert from range [0, 1023] range [0, 1] (working in double image format).
I = I/(2^10-1);
%Bayer mosaic color channel separation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Assume input format is GBRG Bayer mosaic format.
%Separate to color components.
B = I(1:2:end, 1:2:end);
G = I(1:2:end, 2:2:end);
IR = I(2:2:end, 1:2:end);
R = I(2:2:end, 2:2:end);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Linear stretching each color channel.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Linear streatch blue color channel.
B = imadjust(B, stretchlim(B, [0.02 0.98]),[]);
%Linear streatch green channel.
G = imadjust(G, stretchlim(G, [0.02 0.98]),[]);
%Linear streatch red color channel.
R = imadjust(R, stretchlim(R, [0.02 0.98]),[]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Simple white balance
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Median or R, G and B.
rgb_med = [median(R(:)), median(G(:)), median(B(:))];
rgb_scale = max(rgb_med)./rgb_med;
%Scale each color channel, to have the same median.
R = R*rgb_scale(1);
G = G*rgb_scale(2);
B = B*rgb_scale(3);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Restore Bayer mosaic.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Insert streached color channnels back into I.
I(1:2:end, 1:2:end) = B;
I(1:2:end, 2:2:end) = G;
%I(2:2:end, 1:2:end) = G; %Replace IR with Green.
I(2:2:end, 2:2:end) = R;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Replace IR with green - resize green to full size of image first.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
T = imresize(G, [srcM, srcN]); %T - temporary green, size 1280x720
I(2:2:end, 1:2:end) = T(2:2:end, 1:2:end); %Replace IR with Green.
I = max(min(I, 1), 0); %Limit I to range [0, 1].
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Simple gamma correction
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
gamma = 0.45;
I = I.^gamma;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Demosaic
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Convert to uint8 (range [0, 255]).
I = uint8(round(I*255));
RGB = demosaic(I, 'bggr');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
imshow(RGB);
结果:
现在颜色正常了...
户外图像处理:
对室外图像进行“室内”处理,得到如下结果:
白树是近红外光谱渗透到 R、G 和 B 像素(不仅是红外像素)的标志。
植被的叶绿素在近红外光谱中具有高反射。请参阅:http://missionscience.nasa.gov/ems/08_nearinfraredwaves.html ,然后在 Google 上搜索。
需要从红色、绿色和蓝色 channel 中减去 IR。
我使用了以下图像处理阶段:
以下 Matlab 代码将室外图像处理为 RGB:
srcN = 1280;
srcM = 720;
f = fopen('ir_6.raw', 'r');
%Read as transposed matrix dimensions, and transpose the matrix.
%The reason for that, is that Matlab memory oreder is column major, and
%raw image is stored in row major (like C arrays).
I = fread(f, [srcN, srcM], 'uint16');
fclose(f);
I = I';
%Convert from range [0, 1023] range [0, 1] (working in double image format).
I = I/(2^10-1);
%Bayer mosaic color channel separation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Assume input format is GBRG Bayer mosaic format.
%Separate to color components.
B = I(1:2:end, 1:2:end);
G = I(1:2:end, 2:2:end);
IR = I(2:2:end, 1:2:end);
R = I(2:2:end, 2:2:end);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Subtract IR "surplus" from R, G and B.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%The coefficients were tuned by trial and error...
ir_r = 1.3; % 130% of IR radiation is absorbed by red pixels???
ir_g = 0.35; % 35% of IR radiation is absorbed by green pixels.
ir_b = 0.3; % 30% of IR radiation is absorbed by blue pixels.
IR = imresize(IR, size(I)); %Resize IR to the size of I.
IR = max(min(IR, 1), 0); %Limit IR to range [0, 1] (because imresize values slightly outside the range of input).
R = R - IR(2:2:end, 2:2:end)*ir_r; %Subtract IR for R (IR scale coefficient is ir_r).
G = G - IR(1:2:end, 2:2:end)*ir_g; %Subtract IR for G (IR scale coefficient is ir_g).
B = B - IR(1:2:end, 1:2:end)*ir_b; %Subtract IR for B (IR scale coefficient is ir_b).
R = max(min(R, 1), 0); %Limit IR to range [0, 1]
G = max(min(G, 1), 0);
B = max(min(B, 1), 0);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Linear stretching each color channel.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Linear streatch blue color channel.
B = imadjust(B, stretchlim(B, [0.02 0.98]),[]);
%Linear streatch green channel.
G = imadjust(G, stretchlim(G, [0.02 0.98]),[]);
%Linear streatch red color channel.
R = imadjust(R, stretchlim(R, [0.02 0.98]),[]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Simple white balance
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Median or R, G and B.
rgb_med = [median(R(:)), median(G(:)), median(B(:))];
rgb_scale = max(rgb_med)./rgb_med;
%Scale each color channel, to have the same median.
R = R*rgb_scale(1);
G = G*rgb_scale(2);
B = B*rgb_scale(3);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Restore Bayer mosaic.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Insert streached color channnels back into I.
I(1:2:end, 1:2:end) = B;
I(1:2:end, 2:2:end) = G;
I(2:2:end, 2:2:end) = R;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Replace IR with green - resize green to full size of image first.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
T = imresize(G, [srcM, srcN]); %T - temporary green, size 1280x720
I(2:2:end, 1:2:end) = T(2:2:end, 1:2:end); %Replace IR with Green.
I = max(min(I, 1), 0); %Limit I to range [0, 1].
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Simple gamma correction
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
gamma = 0.45;
I = I.^gamma;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Demosaic
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Convert to uint8 (range [0, 255]).
I = uint8(round(I*255));
RGB = demosaic(I, 'bggr');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
RGB = imresize(RGB, size(I)/2); %Shrink size of RGB image for reducing demosaic artifacts.
imshow(RGB);
结果不是很好,但它演示了可以从红绿蓝 channel 中减去红外 channel 的概念。
还有工作要做...
结果图片:
出现“假色”绿斑的原因:
红色 channel 中的饱和像素(在原始输入中饱和)未正确处理。
可以通过减少曝光(以较短的曝光时间拍摄照片)来解决问题。
关于python - 如何读取 10 位 Raw 图像?其中包含 RGB-IR 数据,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/39623001/
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