Question

我想知道如何从我的10位原始数据（它有rgb-ir imagedata）数据中提取rgb图像？

如何阅读Python或MATLAB？

拍摄时的相机分辨率为1280x720：室内照片Image for download 户外照片Image 2 for download

相机型号：e-CAM40_CUMI4682_MOD

非常感谢

Answer 1

我使用了以下图像处理阶段：

拜耳马赛克彩色通道分离。
线性拉伸每个颜色通道。
简单的白平衡。
用绿色替换IR颜色通道（将图像转换为标准拜耳格式）。
恢复拜耳马赛克。
简单的伽马校正。
去马赛克

我没有处理IR颜色通道，而是将其替换为绿色通道。

根据您添加的RGB图像，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......

户外图像处理：

应用＆＃34;室内＆＃34;处理室外图像，得到以下结果：

白树是近红外光谱穿透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上进行搜索需要从红色，绿色和蓝色通道中减去IR。

我使用了以下图像处理阶段：

拜耳马赛克彩色通道分离。
减去IR＆＃34;盈余＆＃34;来自红色，绿色和蓝色通道。
线性拉伸每个颜色通道。
简单的白平衡。
恢复拜耳马赛克。
简单的伽马校正。
去马赛克。
将RGB图像调整为较低分辨率。

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);

结果并不是那么好，但它展示了可以从红绿蓝通道中减去红外通道的概念。
仍有工作要做......
结果图片：

＆＃34;假色的原因＆＃34;绿色补丁：
红色通道中的饱和像素（原始输入中饱和）未得到妥善处理可以通过减少曝光来解决问题（以较低的曝光时间拍摄）。

如何读取10位原始图像？其中包含RGB-IR数据

1 个答案: