Bayer transformation The
photodiodes employed in an
image sensor are color-blind by nature: they can only record
shades of grey. To get
color into the picture, they are covered with different color filters:
red,
green and
blue (
RGB) according to the pattern designated by the
Bayer filter. As each photodiode records the color information for exactly one
pixel of the image, without an image processor there would be a green pixel next to each red and blue pixel. This process, however, is quite complex, and involves a number of different operations. Its quality depends largely on the effectiveness of the
algorithms applied to the raw data coming from the sensor. The mathematically manipulated data becomes the recorded photo file.
Demosaicing As stated above, the image processor evaluates the color and
brightness data of a given pixel, compares them with the data from neighboring pixels, and then uses a
demosaicing algorithm to produce an appropriate color and brightness value for the pixel. The image processor also assesses the whole picture to guess at the correct distribution of
contrast. By adjusting the
gamma value (heightening or lowering the contrast range of an image's mid-tones), subtle tonal gradations, such as in
human skin or the blue of the
sky, become much more realistic.
Noise reduction Noise is a phenomenon found in any
electronic circuitry. In
digital photography its effect is often visible as random spots of obviously wrong color in an otherwise smoothly-colored area. Noise increases with temperature and
exposure times. When higher
ISO settings are chosen the electronic signal in the image sensor is amplified, which at the same time increases the noise level, leading to a lower
signal-to-noise ratio. The image processor attempts to separate the noise from the image information and to remove it. This can be quite a challenge, as the image may contain areas with fine textures which, if treated as noise, may lose some of their definition.
Image sharpening As the color and brightness values for each pixel are
interpolated some
image sharpening is applied to even out any fuzziness that has occurred. To preserve the impression of
depth, clarity and fine details, the image processor must sharpen edges and contours. It therefore must
detect edges correctly and reproduce them smoothly and without over-sharpening. ==Models==