# How to add camera noise to an AI image, properly

Source: https://passreal.com/how-to/add-camera-noise

Sensor noise is not uniform static. Shot noise scales with the square root of the signal, so bright areas carry more of it than shadows; read noise is roughly constant; and after demosaicing the noise is correlated between neighbouring pixels rather than independent. Adding flat Gaussian noise reproduces none of that, which is why it reads as added noise rather than as a sensor.

## What sensor noise actually is

Three separate phenomena get bundled under one word, and they behave
differently.

Shot noise comes from the arrival statistics of light itself. Photons hit a
photosite at random intervals, so the count in a fixed exposure follows a
Poisson distribution, whose standard deviation is the square root of its mean.
The practical consequence is that noise grows with brightness in absolute terms
while shrinking relative to the signal. Highlights carry more noise than
shadows, not less.

Read noise comes from the electronics converting charge to a number. It is
roughly constant regardless of signal, which is why it dominates in dark
regions, and why shadow areas of an underexposed photograph look grainy.

Pattern noise comes from photosites not being identical. It is fixed for a given
sensor, which is what makes photo-response non-uniformity usable for camera
identification.

## Why uniform noise gives itself away

The default noise tool in an image editor adds an independent random value to
every pixel, drawn from the same distribution everywhere. Two properties of that
are wrong.

It does not scale with signal. A flat highlight and a flat shadow receive the
same disturbance, which no sensor produces.

It is independent between neighbouring pixels. Real sensor noise is not,
because demosaicing interpolates missing colour values from neighbours, which
correlates their errors. The autocorrelation of noise in a demosaiced image has
structure; the autocorrelation of editor noise is a spike at zero and nothing
else.

Both properties are straightforward to measure on a flat patch, and both survive
JPEG compression well enough to remain measurable.

## Doing it in the right order

Noise has to be applied to linear-ish pixel values before resampling and before
compression, because both operations average neighbours and destroy the grain
structure. Applied afterwards, the grain sits on top of a smoothed image as a
separate layer.

This is why the pipeline runs the sensor stage early: grain at intensity
0.018, then lateral chromatic aberration reaching 1.25 px at the
corners, then the frequency filter at 0.88 of Nyquist, then the encode.
Reordering those stages produces an image where each effect contradicts the one
before it.

## Doing it by hand, if you want to

The manual route is worth understanding even if you end up automating it,
because it makes the failure modes concrete.

- Work in a linear colour space if your tool offers one. Noise added to
  gamma-encoded values lands unevenly across the tonal range, which reverses the
  relationship you are trying to reproduce.
- Generate the noise at full resolution and never scale it. Scaling a noise
  layer is the single most common way to end up with grain that has visible
  structure at the wrong size.
- Apply more to the green channel than to red and blue if your tool separates
  them, matching the denser green sampling of a Bayer layout.
- Modulate by luminance so highlights receive more absolute disturbance than
  shadows, which is what shot noise does.
- Then stop. The temptation is to keep going until the grain is visible, and
  visible grain is the failure condition, not the goal.

An editor can approximate the first four with masks and blend modes. What it
cannot easily reproduce is the correlation between neighbouring pixels that
demosaicing introduces, which is why the result tends to hold up visually and
not statistically.

## The intensity trap

Grain intensity is the dominant lever in this pipeline and it moves two things
in opposite directions. Turning it down improves the fidelity numbers and makes
visual regression checks pass. It also makes the image easier to classify,
because the noise floor it was supposed to supply is no longer there.

Any change to this value has to be measured on both axes at once - fidelity and
detectability - or it is not a measurement, it is a preference. That is why the
setting is 0.018 rather than something rounder.

## Questions

### Can I just use Add Noise in Photoshop?

You can, and it will look plausible to a person. It will not behave like sensor output under measurement, because Photoshop's noise is uniform across brightness levels and independent between pixels, while sensor noise is neither.

### How much noise is the right amount?

Less than most people apply. PassReal uses an intensity of 0.018. Raising it makes regression fixtures pass while making the image measurably easier to classify, which is the trap: visible grain looks convincing to a viewer and reads as an added layer to a model.

### Should noise be added before or after resizing?

Before any resampling, and before compression. Resizing after adding noise averages neighbouring values and destroys the grain structure, leaving a smoothed texture that matches neither the original nor a sensor.

### Does the noise need to differ per channel?

Yes. A colour sensor samples one colour per photosite and interpolates the rest, so the green channel is sampled twice as densely as red or blue in the common Bayer layout. Noise that is identical across all three channels is not what a demosaiced image looks like.

### Does adding grain hurt image quality?

It costs a measurable amount of PSNR, reported per image on the sheet above. At the intensity used here the cost is small and the grain is invisible at viewing size, which is the point: grain you can see is grain a classifier can isolate.

Try it: https://passreal.com/app (three images free)