Digital Signal Processing Reference
In-Depth Information
In Gaussian noise, each pixel in the image will be changed from its original value by a
(usually) small amount. A histogram, a plot of the amount of distortion of a pixel value
against the frequency with which it occurs, shows a normal distribution of noise. While
other distributions are possible, the Gaussian (normal) distribution is usually a good
model, due to the central limit theorem that says that the sum of different noises tends to
approach a Gaussian distribution.
In either case, the noises at different pixels can be either correlated or uncorrelated; in
many cases, noise values at different pixels are modeled as being independent and
identically distributed, and hence uncorrelated.
Removal
Tradeoffs
In selecting a noise reduction algorithm, one must weigh several factors:
the available computer power and time available: a digital camera must apply
noise reduction in a fraction of a second using a tiny onboard CPU, while a
desktop computer has much more power and time
whether sacrificing some real detail is acceptable if it allows more noise to be
removed (how aggressively to decide whether variations in the image are noise or
not)
the characteristics of the noise and the detail in the image, to better make those
decisions
Chroma and luminance noise separation
In real-world photographs, the highest spatial-frequency detail consists mostly of
variations in brightness ("luminance detail") rather than variations in hue ("chroma
detail"). Since any noise reduction algorithm should attempt to remove noise without
sacrificing real detail from the scene photographed, one risks a greater loss of detail from
luminance noise reduction than chroma noise reduction simply because most scenes have
little high frequency chroma detail to begin with. In addition, most people find chroma
noise in images more objectionable than luminance noise; the colored blobs are
considered "digital-looking" and unnatural, compared to the grainy appearance of
luminance noise that some compare to film grain. For these two reasons, most
photographic noise reduction algorithms split the image detail into chroma and luminance
components and apply more noise reduction to the former; the in-camera noise reduction
algorithm used by Nikon's DSLR's, in particular, is known for this.
Most dedicated noise-reduction computer software allows the user to control chroma and
luminance noise reduction separately.
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