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Image noise is random variation of brightness or color information in . It can originate in film grain and in the unavoidable shot noise of an ideal photon detector. In digital photography is usually an aspect of electronic noise, produced by the image sensor of a digital camera. The circuitry of a Image scanner can also contribute to the effect. Image noise is often (but not necessarily) an undesirable by-product of image capture that obscures the desired information. Typically the term “image noise” is used to refer to noise in 2D images, not 3D images.
The original meaning of "noise" was "unwanted signal"; unwanted electrical fluctuations in signals received by AM radios caused audible acoustic noise ("static"). By analogy, unwanted electrical fluctuations are also called "noise".
Image noise can range from almost imperceptible specks on a digital photograph taken in good light, to optical and Radioastronomy images that are almost entirely noise, from which a small amount of information can be derived by sophisticated processing. Such a noise level would be unacceptable in a photograph since it would be impossible even to determine the subject.
A typical model of image noise is Gaussian, additive, independent at each pixel, and independent of the signal intensity, caused primarily by Johnson–Nyquist noise (thermal noise), including that which comes from the reset noise of capacitors ("kTC noise"). Amplifier noise is a major part of the "read noise" of an image sensor, that is, of the constant noise level in dark areas of the image. In color cameras where more amplification is used in the blue color channel than in the green or red channel, there can be more noise in the blue channel. At higher exposures, however, image sensor noise is dominated by shot noise, which is not Gaussian and not independent of signal intensity. Also, there are many Gaussian denoising algorithms.Mehdi Mafi, Harold Martin, Jean Andrian, Armando Barreto, Mercedes Cabrerizo, Malek Adjouadi, “A Comprehensive Survey on Impulse and Gaussian Denoising Filters for Digital Images,” Signal Processing, vol. 157, pp. 236-260, 2019.
Dead in an LCD monitor produce a similar, but non-random, display.Charles Boncelet (2005), Alan C. Bovik. Handbook of Image and Video Processing. Academic Press.
In addition to photon shot noise, there can be additional shot noise from the dark leakage current in the image sensor; this noise is sometimes known as "dark shot noise" or "dark-current shot noise". Dark current is greatest at "hot pixels" within the image sensor. The variable dark charge of normal and hot pixels can be subtracted off (using "dark frame subtraction"), leaving only the shot noise, or random component, of the leakage. If dark-frame subtraction is not done, or if the exposure time is long enough that the hot pixel charge exceeds the linear charge capacity, the noise will be more than just shot noise, and hot pixels appear as salt-and-pepper noise.
Film grain is usually regarded as a nearly isotropic (non-oriented) noise source. Its effect is made worse by the distribution of silver halide grains in the film also being random.
The f-number is indicative of light density in the focal plane (e.g., photons per square micron). With constant f-numbers, as focal length increases, the lens aperture diameter increases, and the lens collects more light from the subject. As the focal length required to capture a scene at a specific angle of view is roughly proportional to the width of the sensor, given an f-number the amount of light collected is roughly proportional to the area of the sensor, resulting in a better signal-to-noise ratio for larger sensors. With constant aperture diameters, the amount of light collected and the signal-to-noise ratio for shot noise are both independent of sensor size. In the case of images bright enough to be in the shot noise limited regime, when the image is scaled to the same size on screen, or printed at the same size, the pixel count makes little difference to perceptible noise levels – the noise depends primarily on the total light over the whole sensor area, not how this area is divided into pixels. For images at lower signal levels (higher ISO settings) where read noise (noise floor) is significant, more pixels within a given sensor area will make the image noisier if the per pixel read noise is the same.
For example, the noise level produced by a Four Thirds sensor at ISO 800 is roughly equivalent to that produced by a full frame sensor (with roughly four times the area) at ISO 3200, and that produced by a 1/2.5" compact camera sensor (with roughly 1/16 the area) at ISO 100. All cameras will have roughly the same ISO setting for a given scene at the same shutter speed and the same f-number – resulting in substantially less noise with the full frame camera. Conversely, if all cameras were using lenses with the same aperture diameter, the ISO settings would be different across the cameras, but the noise levels would be roughly equivalent.
A simplified example of the impossibility of unambiguous noise reduction: an area of uniform red in an image might have a very small black part. If this is a single pixel, it is likely (but not certain) to be spurious and noise; if it covers a few pixels in an absolutely regular shape, it may be a defect in a group of pixels in the image-taking sensor (spurious and unwanted, but not strictly noise); if it is irregular, it may be more likely to be a true feature of the image. But a definitive answer is not available.
This decision can be assisted by knowing the characteristics of the source image and of human vision. Most noise reduction algorithms perform much more aggressive chroma noise reduction, since there is little important fine chroma detail that one risks losing. Furthermore, many people find luminance noise less objectionable to the eye, since its textured appearance mimics the appearance of film grain.
The high sensitivity image quality of a given camera (or RAW development workflow) may depend greatly on the quality of the algorithm used for noise reduction. Since noise levels increase as ISO sensitivity is increased, most camera manufacturers increase the noise reduction aggressiveness automatically at higher sensitivities. This leads to a breakdown of image quality at higher sensitivities in two ways: noise levels increase and fine detail is smoothed out by the more aggressive noise reduction.
In cases of extreme noise, such as astronomical images of very distant objects, it is not so much a matter of noise reduction as of extracting a little information buried in a lot of noise; techniques are different, seeking small regularities in massively random data.
Digital noise is sometimes present on videos encoded in MPEG-2 format as a compression artifact.
Image:Flower_at_100_ISO_for_comparison.JPG|
Image:Flower_at_1600_ISO_for_comparison.JPG|
Image:ISO_comparison_150px.jpg|Comparison of both images. This is a crop of a small section of each image displayed at 100%. The top portion was shot at 100 ISO, the bottom portion at 1600 ISO.
The ISO setting on a digital camera is the first (and sometimes only) user adjustable (Analog signal) gain setting in the Signal chain. It determines the amount of gain applied to the voltage output from the image sensor and has a direct effect on read noise. All signal processing units within a digital camera system have a noise floor. The difference between the signal level and the noise floor is called the signal-to-noise ratio. A higher signal-to-noise ratio equates to a better quality image.
In bright sunny conditions, a slow shutter speed, wide open aperture, or some combination of all three, there can be sufficient photons hitting the image sensor to completely fill, or otherwise reach near capacity of the pixel wells. If the capacity of the pixel wells is exceeded, this equates to over exposure. When the pixel wells are at near capacity, the photons themselves that have been exposed to the image sensor, generate enough energy to excite the emission of in the image sensor and generate sufficient voltage at the image sensor output, equating to a lack of need for ISO gain (higher ISO above the base setting of the camera). This equates to a sufficient signal level (from the image sensor) which is passed through the remaining signal processing electronics, resulting in a high signal-to-noise ratio, or low noise, or optimal exposure.
Conversely, in darker conditions, faster shutter speeds, closed apertures, or some combination of all three, there can be a lack of sufficient photons hitting the image sensor to generate a suitable voltage from the image sensor to overcome the noise floor of the signal chain, resulting in a low signal-to-noise ratio, or high noise (predominately read noise). In these conditions, increasing ISO gain (higher ISO setting) will increase the image quality of the output image, as the ISO gain will Amplifier the low voltage from the image sensor and generate a higher signal-to-noise ratio through the remaining signal processing electronics.
It can be seen that a higher ISO setting (applied correctly) does not, in and of itself, generate a higher noise level, and conversely, a higher ISO setting reduces read noise. The increase in noise often found when using a higher ISO setting is a result of the amplification of shot noise and a lower dynamic range as a result of technical limitations in current technology.
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