Several types of noise and artifacts affect the quality of images obtained even with imaging systems of the highest quality and under the most carefully designed experimental conditions. As a consequence, the removal of noise and artifacts is an important preprocessing step in the analysis of images; see Rangayyan [6] for detailed discussions on various sources of noise and methods to filter grayscale or scalar images.

Some of the sources and types of noise and artifacts that affect images are described in the following list [6, 19].

• The Poisson nature of the detection of photons of light.

• Thermal noise in the detector (for example, the dark current in CCD detectors).

• Photoelectronic noise in electronic detectors.

• Shot noise due to inactive elements in an electronic detector.

• Noise due to quantization.

• Noise due to lossy data compression or transmission, amplification, filtering, or other types of imperfect signal processing procedures.

• Punctate, impulsive, or shot noise due to dust or fine particles on the object being imaged or the detector, leading to pixels that are of a widely different color than their neighbors.

• Scratches on the object being imaged or on the detector (especially film) that could appear as intense line segments.

• Salt-and-pepper noise due to impulsive noise, leading to black or white pixels at the extreme ends of the pixel-value range in grayscale or intensity images, or colors that are unrelated to those of neighboring pixels.

• Film-grain noise due to scanning of films with high spatial resolution.

In several algorithms for filtering color images, the observed noise is modeled as an additive, white, Gaussian noise process that affects each color component independently; it is assumed that the noise process is independent of the image-generating process. However, impulsive noise, modeled as sparse "spikes" that appear in the images, may also corrupt color images. For the sake of generality, it may be appropriate to assume that color images are corrupted by a combination of these two types of noise. Regardless, it should be noted that some processes can involve multiplicative noise and nonlinear effects [6].