![]() ![]() The intense noise in the blue channel will also affect the other channels due to demosaicing. The typical color noise scenario in imaging is strong noise in the blue channel and lower noise in the green and red channels. ![]() During the interpolation process, the noise in these pixels will smear out. As a result, the noise of an individual pixel will affect the color information of a neighboring pixel. Essentially, the missing color information is interpolated from a neighboring pixel to ensure Red, Green, and Blue are obtained in each pixel. This process is valid for nearly all types of sensors.Ĭolor noise is attributed to a process known as demosaicing. Essentially, a single-pixel captures only color information for a specific band of the light spectrum (e.g., Red, Green, or Blue). Color sensors, however, will display both intensity noise and color noise.Ĭolor noise is created and amplified during the generation of color information. These sensors will only show the noise as a variation in the intensity. In the previous section, we only analyzed single pixels' behavior and their neighbors from Monochrome sensors. Image 3: Averaging images together to reduce the presence of noise. Dark Signal Non-Uniformity (DSNU) is another form that has a slight variance between pixels in their signal, or, more simply put, the generated signal in the absence of light. One form of spatial noise is Pixel Response Non-Uniformity (PRNU), a slight variation in each pixel's sensitivity. The remaining (averaged) image will show then only show the spatial noise. This process is typically carried out by averaging hundreds of images to minimize the random component. The various forms of spatial noise are only observable when minimizing the temporal noise. Keep in mind that variations between the pixel can also be caused by temporal noise. As a result, each pixel will show a slightly different behavior resulting in slightly contrasting digital values. Pixels positioned next to each other on the sensor will display differences in their digital values even if the object is equal. EMVA1288 uses the term "non-uniformity," while ISO 15739 uses "fixed pattern noise." This noise type is often referred to as "non-uniformities" because the term noise itself implies a random process. Variations in an individual pixel typically cause spatial noise and are therefore not random. Spatial noise (pattern noise or non-uniformity noise) Even though the images' scene doesn't change, we will still see a variation in the digital value we receive from this particular pixel. If we observe the same pixel in an image captured multiple times, we will see this pixel fluctuate between the various images, as demonstrated in image 2. This procedure is known as photon shot noise. Also, the number of photons that hit a single-pixel during the exposure time will vary. Temporal noise is almost always completely random and results from variations in generating a digital value from a single pixel by converting incoming photons into electrons. In this article, we try our best to cover the most widely used definitions and solutions. Unfortunately, the terms and definitions differ slightly across standards and publications, making it difficult to always agree on one or two solutions. In digital imaging, we encounter various types of noise. ![]()
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