1.Bayer mode image
Bayer mode is a common mode used for digital image capture, especially widely used in single-sensor cameras. Bayer mode is a technology that captures color images by arranging red, green, and blue color filter arrays and interpolation algorithms.
A Bayer pattern image is composed of a color filter array containing only one of the three colors red, green, and blue, where each pixel can only perceive light of one of the colors. The arrangement of this color filter array usually follows a fixed pattern, such as BGGR, RGGB or similar.
For a Bayer mode image, each pixel has only one color information, and the information of the other two colors needs to be estimated through an interpolation algorithm. Interpolation algorithms use color information from nearby pixels to extrapolate missing color information, producing a complete color image.
To convert a Bayer mode image into a complete color image, a series of processing steps are required, such as color interpolation, white balance correction, color correction, etc. These processing steps calculate the final color value based on each pixel’s raw data and information from neighboring pixels. The final image will contain full RGB information, allowing us to see true color images.
2. Arrangement instructions in bayer mode
The arrangement of Bayer mode images is usually represented by a four-letter abbreviation, where each letter represents a color channel (red, green, blue).
Here are descriptions of some common Bayer pattern arrangements:
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RGGB: In RGGB Bayer mode, the first pixel in the first row is red (R), the second pixel in the first row is green (G), and the first pixel in the second row is green (G). The second pixel in the second row is blue (B). This arrangement is widely used in many sensors.
-
GRBG: In GRBG Bayer mode, the first pixel of the first row is green (G), the second pixel of the first row is red (R), and the first pixel of the second row is blue (B) , the second pixel in the second row is green (G). This arrangement is also common in many sensors.
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BGGR: In BGGR Bayer mode, the first pixel of the first row is blue (B), the second pixel of the first row is green (G), and the first pixel of the second row is green (G) , the second pixel in the second row is red (R). This arrangement is also common.
3. Common ways to convert rgb888 in Bayer mode
A common way to convert a Bayer mode image to an RGB888 image is color interpolation. This method uses an interpolation algorithm to complete the estimation of missing color channels.
There are two common color interpolation algorithms:
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Linear interpolation: In this interpolation algorithm, missing color pixels are interpolated using the values of their neighboring pixels. For example, for a BGGR Bayer mode image, the missing green pixel can be found by averaging the four neighboring pixels around it. For missing red or blue pixels, this can be calculated using values from adjacent rows or columns.
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Modified bilinear interpolation: This is a more accurate interpolation algorithm that improves upon linear interpolation. In this algorithm, missing color pixels are interpolated using neighboring pixels and their neighboring pixels. This algorithm can provide better results when processing high distortion images.
4. Detailed introduction to linear interpolation method
Linear interpolation is a simple interpolation algorithm that is commonly used to convert Bayer pattern images to RGB images. It calculates missing values by using the values of neighboring pixels.
Specifically, in BGGR Bayer mode, missing values for the green channel can be linearly interpolated by using the average of the surrounding green pixel values (left, right, above, and below). The same approach also works in the case of red and blue channels, but interpolation needs to be done using values from adjacent columns or adjacent rows.
Below is a simple diagram illustrating how linear interpolation is done in the case of the green channel. Assuming that a green pixel (green point) is surrounded by only four neighboring pixels (blue point), you can use the average of these pixels to calculate the value of that pixel (yellow point):
B G B G B G G * G * G * * (pixels to be calculated) B G B G B G G*G*G* B G B G B G
It is similar in the case of red and blue channels. After determining the directions of adjacent pixels, the values of these pixels are used for interpolation calculations.
5. Implementation
A combination of two methods is used here, namely the “101 method” and the “RGB interpolation method” to process image data.
First, calculate the width and height of the source image based on the width and height of the input image, and create a new array src_data whose size is the width of the source image multiplied by the height.
Next, in the “101 method”, use a double-layer loop to copy the pixel data of the original image into the src_data array, using a certain offset position to ensure the correct position of each pixel.
Then, the src_data array is filled with boundaries according to the “RGB interpolation method”. The specific operation is: copy the pixels of the second row to the first row, copy the pixels of the second-to-last row to the first-to-last row; copy the pixels of the second column to the first column, and copy the pixels of the second-to-last column to the first row. Copy to the last column. This ensures that pixels at the boundary can also participate in subsequent interpolation calculations.
Finally, each pixel of the source image is traversed through two levels of nested loops. For each pixel, the “RGB interpolation method” is used to calculate the values of the three components R, G, and B based on its position in the source image and the values of adjacent pixels. How it’s calculated depends on the location and color arrangement of the pixels in the source image.
Finally, the calculated values of the R, G, and B components are written to the image_rgb_data_888 array of the target image, and the value of dataIndex is updated for the next write.
Depending on the color arrangement (e.g. “BGGR”, “GRBG”, “GBRG”), the order in which the R, G, and B components are calculated is not exactly the same. The value of each component is calculated by interpolating the values of adjacent pixels in the source image. After this processing, the final image_rgb_data_888 array is the converted RGB image data.
int src_width = width + 2;
int src_height = height + 2;
unsigned char *src_data = new unsigned char [src_width * src_height];
/* 101 filling method (example)
R G R G R G
G *B *G *B *G B
R *G *R *G *R G
G *B *G *B *G B
R *G *R *G *R G
G B G B G B
*/
for(int i = 0; i < height; i + + )
{
for(int j = 0; j < width; j + + )
{
src_data[src_width*(i + 1) + 1 + j] = data[width*i + j];
}
}
/* Fill 2 lines */
for(int i = 0; i < width; i + + )
{
/*Line 1*/
src_data[1 + i] = src_data[src_width*2 + 1 + i];
/*The 1st line from the bottom*/
src_data[src_width*(src_height - 1) + 1 + i] = src_data[src_width*(src_height - 3) + 1 + i];
}
/* Fill 2 columns */
for(int i = 0; i < src_height; i + + )
{
/*Column 1*/
src_data[i * src_width + 0] = src_data[i * src_width + 2];
/*The 1st column from the bottom*/
src_data[i * src_width + src_width - 1] = src_data[i * src_width + src_width - 3];
}
int dataIndex = 0; // Record data index
for(int i = 0; i < height; i + + )
{
for(int j = 0; j < width; j + + )
{
int index_1 = src_data[i*src_width + j];
int index_2 = src_data[i*src_width + j + 1];
int index_3 = src_data[i*src_width + j + 2];
int index_4 = src_data[(i + 1)*src_width + j];
int index_5 = src_data[(i + 1)*src_width + j + 1];
int index_6 = src_data[(i + 1)*src_width + j + 2];
int index_7 = src_data[(i + 2)*src_width + j];
int index_8 = src_data[(i + 2)*src_width + j + 1];
int index_9 = src_data[(i + 2)*src_width + j + 2];
unsigned char index_r = 0;
unsigned char index_g = 0;
unsigned char index_b = 0;
/*bggr*/
if((i % 2 == 0) & amp; & amp; (j % 2 == 0)) //B
{
//r
index_r = (index_1 + index_3 + index_7 + index_9)/4;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = index_5;
}
if((i % 2 == 0) & amp; & amp; (j % 2 != 0)) //g
{
//r
index_r = (index_2 + index_8)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_4 + index_6)/2;
}
if((i % 2 != 0) & amp; & amp; (j % 2 == 0)) //g
{
//r
index_r = (index_4 + index_6)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_2 + index_8)/2;
}
if((i % 2 != 0) & amp; & amp; (j % 2 != 0)) //r
{
//r
index_r = index_5;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = (index_1 + index_3 + index_7 + index_9)/4;
}
/*grbg*/
if((i % 2 == 0) & amp; & amp; (j % 2 == 0)) //g
{
//r
index_r = (index_4 + index_6)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_2 + index_8)/2;
}
if((i % 2 == 0) & amp; & amp; (j % 2 != 0)) //r
{
//r
index_r = index_5;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = (index_1 + index_3 + index_5 + index_7)/4;
}
if((i % 2 != 0) & amp; & amp; (j % 2 == 0)) //b
{
//r
index_r = (index_1 + index_3 + index_5 + index_7)/4;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = index_5;
}
if((i % 2 != 0) & amp; & amp; (j % 2 != 0)) //g
{
//r
index_r = (index_2 + index_8)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_4 + index_6)/2;
}
/*gbrg*/
if((i % 2 == 0) & amp; & amp; (j % 2 == 0)) //g
{
//r
index_r = (index_2 + index_8)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_4 + index_6)/2;
}
if((i % 2 == 0) & amp; & amp; (j % 2 != 0)) //b
{
//r
index_r = (index_1 + index_3 + index_7 + index_9)/4;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = index_5;
}
if((i % 2 != 0) & amp; & amp; (j % 2 == 0)) //r
{
//r
index_r = index_5;
//g
index_g = (index_2 + index_4 + index_6 + index_8)/4;
//b
index_b = (index_1 + index_3 + index_7 + index_9)/4;
}
if((i % 2 != 0) & amp; & amp; (j % 2 != 0)) //g
{
//r
index_r = (index_4 + index_6)/2;
//g
index_g = (index_1 + index_3 + index_5 + index_7 + index_9)/5;
//b
index_b = (index_2 + index_8)/2;
}
image_rgb_data_888[dataIndex] = index_r;
image_rgb_data_888[dataIndex + 1] = index_g;
image_rgb_data_888[dataIndex + 2] = index_b;
dataIndex + = 3; //The data index is incremented by 3 because RGB888 data occupies 3 bytes per pixel
}