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I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting.

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to filter out different wavelengths of light. For example, a green pass filter will only let green light through.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.

I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting.

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to attenuate different wavelengths of light. For example, a green pass filter will let more green light through than red or blue light, though a bit of each will make it through the green filter just as the medium wavelength cones in our human retinas react a little to red and blue light though they respond much more strongly to green.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.

I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting:

Data Genetics: How Digital Cameras Work

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to filter out different wavelengths of light. For example, a green pass filter will only let green light through.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.

I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting.

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to filter out different wavelengths of light. For example, a green pass filter will only let green light through.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.

I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting.

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to filter out different wavelengths of light. For example, a green pass filter will only let green light through.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.

I know it's already been answered quite well by mattdm, but I just thought you might find this article interesting:

Data Genetics: How Digital Cameras Work

In case the link goes down, here is a summary:

The human eye is most sensitive to colors in the green wavelength region (coincidental with the fact that our sun emits most intensely in the green region).

The camera eye (charge coupled device (CCD) or complimentary metal oxide semiconductor (CMOS)) is sensitive only to light intensity, not to color.

Optical filters are used to filter out different wavelengths of light. For example, a green pass filter will only let green light through.

Optical filters used in digital cameras are the size of the individual pixel sensors, and are arranged in a grid to match the sensor array. Red, green and blue (sort of like our cone cells) filters are used. However, because our eyes are more sensitive to green, the Bayer array filter has 2 green pixel filters for each red and blue pixel. The Bayer array has green filters forming a checkerboard like pattern, while red and blue filters occupy alternating rows.

Getting back to your original question: what does an unprocessed RAW file look like?

It looks like a black an white checkered lattice of the original image.

The fancy software for post-processing the RAW files first applies the Bayer filter. It looks more like the actual image after this, with color in the correct intensity and locations. However, there are still artifacts of the RGB grid from the Bayer filter, because each pixel is only one color.

There are a variety of methods for smoothing out the color coded RAW file. Smoothing out the pixels is similar to blurring though, so too much smoothing can be a bad thing.

Some of the demosaicing methods are briefly described here:

Nearest Neighbor: The value of a pixel (single color) is applied to its other colored neighbors and the colors are combined. No "new" colors are created in this process, only colors that were originally perceived by the camera sensor.

Linear Interpolation: for example, averages the two adjacent blue values and applies the average blue value to the green pixel in between the adjacent blue pixels. This can blur sharp edges.

Quadratic and cubic Interpolation: similar to linear interpolation, higher order approximations for the in-between color. They use more data points to generate better fits. linear only looks at two, quadratic at three, and cubic at four to generate an in between color.

Catmull-Rom Splines: similar to cubic, but takes into consideration the gradient of each point to generate the in-between color.

Half Cosine: used as an example of an interpolation method, it creates half cosines between each pair of like-colors and has a smooth inflected curve between them. However, as noted in the article, it does not offer any advantage for Bayer arrays due to the arrangement of the colors. It is equivalent to linear interpolation but at higher computational cost.

Higher end post-processing software has better demosaicing methods and clever algorithms. For example, they can identify sharp edges or high contrast changes and preserve their sharpness when combining the color channels.