diff --git a/noise.mbt b/noise.mbt
new file mode 100644
index 0000000..9a63041
--- /dev/null
+++ b/noise.mbt
@@ -0,0 +1,92 @@
+///|
+/// Deterministic noise generators for testing and denoise demos. All
+/// randomness comes from a 64-bit LCG seeded by the caller, so a given
+/// seed produces the identical image on every backend and every run —
+/// keeping the library's reproducibility promise even for "random" ops.
+
+///|
+/// 64-bit linear congruential step (Knuth MMIX constants). UInt64
+/// arithmetic wraps, which is exactly what an LCG needs.
+fn lcg_next(state : UInt64) -> UInt64 {
+  state * 6364136223846793005UL + 1442695040888963407UL
+}
+
+///|
+/// Extracts a uniform value in [0, 1) from the high bits of the state
+/// (the low bits of an LCG are weak; the top 24 give a clean mantissa).
+fn lcg_uniform(state : UInt64) -> Double {
+  ((state >> 40) & 0xFFFFFFUL).to_uint().reinterpret_as_int().to_double() /
+  16777216.0
+}
+
+///|
+/// Adds zero-mean Gaussian noise with standard deviation `sigma` (in luma
+/// steps) to R, G and B; alpha is untouched. The normal deviate is the
+/// classic CLT approximation (sum of 12 uniforms minus 6), which is
+/// branch-free and deterministic. `sigma <= 0` returns a copy.
+pub fn Image::add_gaussian_noise(
+  self : Image,
+  seed : UInt64,
+  sigma : Double,
+) -> Image {
+  if sigma <= 0.0 {
+    return self.copy()
+  }
+  let out = self.copy()
+  let n = self.pixel_count()
+  let mut state = lcg_next(seed ^ 0x9E3779B97F4A7C15UL)
+  for p in 0..<n {
+    let base = p * 4
+    // One shared deviate per pixel keeps the noise gray (no color fringes).
+    let mut acc = 0.0
+    for _ in 0..<12 {
+      state = lcg_next(state)
+      acc = acc + lcg_uniform(state)
+    }
+    let gauss = acc - 6.0 // ~N(0, 1)
+    let scaled = gauss * sigma
+    let rounded = if scaled >= 0.0 { scaled + 0.5 } else { scaled - 0.5 }
+    let delta = rounded.to_int()
+    for c in 0..<3 {
+      out.data[base + c] = clamp_byte(self.data[base + c].to_int() + delta)
+    }
+  }
+  out
+}
+
+///|
+/// Salt-and-pepper noise: each pixel independently becomes pure white
+/// ("salt") or pure black ("pepper") with probability `density / 2` each.
+/// `density` is clamped into [0, 1]; alpha is preserved. Deterministic
+/// for a given seed.
+pub fn Image::add_salt_pepper(
+  self : Image,
+  seed : UInt64,
+  density : Double,
+) -> Image {
+  let d = if density < 0.0 {
+    0.0
+  } else if density > 1.0 {
+    1.0
+  } else {
+    density
+  }
+  let out = self.copy()
+  if d == 0.0 {
+    return out
+  }
+  let n = self.pixel_count()
+  let mut state = lcg_next(seed ^ 0xD1B54A32D192ED03UL)
+  for p in 0..<n {
+    state = lcg_next(state)
+    let u = lcg_uniform(state)
+    if u < d {
+      let base = p * 4
+      let v : Byte = if u < d / 2.0 { b'\x00' } else { b'\xFF' }
+      out.data[base] = v
+      out.data[base + 1] = v
+      out.data[base + 2] = v
+    }
+  }
+  out
+}
diff --git a/noise_test.mbt b/noise_test.mbt
new file mode 100644
index 0000000..4355290
--- /dev/null
+++ b/noise_test.mbt
@@ -0,0 +1,120 @@
+///|
+/// Noise generator tests: seed determinism, statistical sanity of the
+/// generated noise, degenerate parameters, and alpha preservation.
+
+///|
+test "same seed reproduces the identical noisy image" {
+  let img = solid(16, 16, 128, 128, 128, 255)
+  let a = img.add_gaussian_noise(42UL, 12.0)
+  let b = img.add_gaussian_noise(42UL, 12.0)
+  for i in 0..<(16 * 16 * 4) {
+    assert_eq(a.data[i].to_int(), b.data[i].to_int())
+  }
+  let sp1 = img.add_salt_pepper(7UL, 0.2)
+  let sp2 = img.add_salt_pepper(7UL, 0.2)
+  for i in 0..<(16 * 16 * 4) {
+    assert_eq(sp1.data[i].to_int(), sp2.data[i].to_int())
+  }
+}
+
+///|
+test "different seeds produce different noise" {
+  let img = solid(16, 16, 128, 128, 128, 255)
+  let a = img.add_gaussian_noise(1UL, 12.0)
+  let b = img.add_gaussian_noise(2UL, 12.0)
+  let mut differing = 0
+  for i in 0..<(16 * 16 * 4) {
+    if a.data[i].to_int() != b.data[i].to_int() {
+      differing = differing + 1
+    }
+  }
+  assert_true(differing > 0)
+}
+
+///|
+test "gaussian noise stays roughly zero-mean and actually perturbs pixels" {
+  // On a mid-gray field the mean must stay near 128 and plenty of pixels
+  // must move; the exact values are pinned by the seeded LCG.
+  let img = solid(32, 32, 128, 128, 128, 255)
+  let out = img.add_gaussian_noise(99UL, 10.0)
+  let mut sum = 0
+  let mut changed = 0
+  for y in 0..<32 {
+    for x in 0..<32 {
+      let v = out.get_pixel(x, y).0.to_int()
+      sum = sum + v
+      if v != 128 {
+        changed = changed + 1
+      }
+    }
+  }
+  let mean = sum / (32 * 32)
+  assert_true(mean >= 123 && mean <= 133) // ~128 +/- 5
+  assert_true(changed > 512) // most pixels perturbed
+  // Alpha untouched.
+  assert_eq(out.get_pixel(0, 0).3.to_int(), 255)
+}
+
+///|
+test "sigma 0 and density 0 are exact copies" {
+  let img = gray_image(4, 1, [10, 100, 180, 250])
+  let g = img.add_gaussian_noise(5UL, 0.0)
+  let s = img.add_salt_pepper(5UL, 0.0)
+  for i in 0..<(4 * 4) {
+    assert_eq(g.data[i].to_int(), img.data[i].to_int())
+    assert_eq(s.data[i].to_int(), img.data[i].to_int())
+  }
+}
+
+///|
+test "salt and pepper hits roughly the requested density with both colors" {
+  // density 0.3 on 64x64 mid-gray: corrupted pixels are exactly 0 or 255,
+  // count within a generous band around 30%, and both colors appear.
+  let img = solid(64, 64, 128, 128, 128, 255)
+  let out = img.add_salt_pepper(2026UL, 0.3)
+  let mut salt = 0
+  let mut pepper = 0
+  let mut untouched = 0
+  for y in 0..<64 {
+    for x in 0..<64 {
+      let v = out.get_pixel(x, y).0.to_int()
+      if v == 255 {
+        salt = salt + 1
+      } else if v == 0 {
+        pepper = pepper + 1
+      } else {
+        assert_eq(v, 128) // anything else must be the original gray
+        untouched = untouched + 1
+      }
+    }
+  }
+  let corrupted = salt + pepper
+  let total = 64 * 64
+  assert_true(corrupted > total * 24 / 100) // >= ~24%
+  assert_true(corrupted < total * 36 / 100) // <= ~36%
+  assert_true(salt > 0 && pepper > 0)
+  assert_eq(corrupted + untouched, total)
+}
+
+///|
+test "median filter visibly cleans salt-and-pepper noise" {
+  // The classic pairing: impulse noise -> 3x3 median. Count wrong pixels
+  // before and after; the median must remove most of them.
+  let img = solid(32, 32, 100, 100, 100, 255)
+  let noisy = img.add_salt_pepper(11UL, 0.08)
+  let cleaned = noisy.median()
+  let mut wrong_before = 0
+  let mut wrong_after = 0
+  for y in 0..<32 {
+    for x in 0..<32 {
+      if noisy.get_pixel(x, y).0.to_int() != 100 {
+        wrong_before = wrong_before + 1
+      }
+      if cleaned.get_pixel(x, y).0.to_int() != 100 {
+        wrong_after = wrong_after + 1
+      }
+    }
+  }
+  assert_true(wrong_before > 40) // noise actually landed
+  assert_true(wrong_after * 4 < wrong_before) // >= 75% removed
+}
diff --git a/pkg.generated.mbti b/pkg.generated.mbti
index aaf8106..86a0895 100644
--- a/pkg.generated.mbti
+++ b/pkg.generated.mbti
@@ -64,6 +64,8 @@ pub(all) struct Image {
   height : Int
   data : FixedArray[Byte]
 }
+pub fn Image::add_gaussian_noise(Self, UInt64, Double) -> Self
+pub fn Image::add_salt_pepper(Self, UInt64, Double) -> Self
 pub fn Image::affine(Self, Affine) -> Self
 pub fn Image::apply_filter_id(Self, Int, Double) -> Self
 pub fn Image::auto_contrast(Self) -> Self