The term noise is related to artefacts in digital images caused by the capture and transfer of the signal. It appears as colored or bright spots in the image.
The higher the camera’s sensitivity setting, the more the signal and the errors are amplified and the noise becomes visible. The amount of noise is larger with smaller pixels because there is less light available for each pixel.
Look at the black patch that is on the left side: in reality the textile didn’t have any white or grey dots. And the grey of the board in the middle of the picture is actually uniform. Do you see the color stains, the "color blur mix"? These errors also appear in the watercolor case to the right.
Here is an image with a high noise level:
And for comparison an image with a low noise level:
In order to keep the picture on an acceptable level, camera manufacturers don’t focus on producing cameras with larger pixels and sensors, but on minimizing and suppressing the noise. Noise can be reduced by using better and larger sensors and better signal processing. The suppression of noise is carried out through image processing within the cameras for which manufacturers have invented a lot of algorithms. In principle this use of software as a noise suppressant leads to a loss of detailed reproduction or resolution.
Resolution is often confused with pixel count, but they refer to completely different things. Resolution is the reproduction of fine detail for which a specific number of pixels in necessary, but not sufficient. Noise suppressing normally keeps edges and high contrasts intact while smoothing out errors in uniform-colored patches. Unfortunately, the algorithms often consider picture details that are below a certain contrast as noise and smooth them out as well. The outcome of this is a loss of detail in an image.
Take a look at our natural scenery. The structures in the leaves with little contrast become lost and the picture quality becomes worse. This makes it appear to be a watercolor image.
This is the complete picture: