diff --git a/node-graph/nodes/raster/src/filter.rs b/node-graph/nodes/raster/src/filter.rs
index 14b6d45bf3..eacbd9fb1c 100644
--- a/node-graph/nodes/raster/src/filter.rs
+++ b/node-graph/nodes/raster/src/filter.rs
@@ -6,7 +6,7 @@ use raster_types::Image;
 use raster_types::{Bitmap, BitmapMut};
 use raster_types::{CPU, Raster};
 
-/// Blurs the image with a Gaussian or blur kernel filter.
+/// Blurs the image with a Gaussian or box blur kernel filter.
 #[node_macro::node(category("Raster: Filter"))]
 async fn blur(
 	_: impl Ctx,
@@ -42,6 +42,36 @@ async fn blur(
 		.collect()
 }
 
+/// Applies a median filter to reduce noise while preserving edges.
+#[node_macro::node(category("Raster: Filter"))]
+async fn median_filter(
+	_: impl Ctx,
+	/// The image to be filtered.
+	image_frame: Table<Raster<CPU>>,
+	/// The radius of the filter kernel. Larger values remove more noise but may blur fine details.
+	#[range((0., 50.))]
+	#[hard_min(0.)]
+	radius: PixelLength,
+) -> Table<Raster<CPU>> {
+	image_frame
+		.into_iter()
+		.map(|mut row| {
+			let image = row.element.clone();
+
+			// Apply median filter
+			let filtered_image = if radius < 0.5 {
+				// Minimum filter radius
+				image.clone()
+			} else {
+				Raster::new_cpu(median_filter_algorithm(image.into_data(), radius as u32))
+			};
+
+			row.element = filtered_image;
+			row
+		})
+		.collect()
+}
+
 // 1D gaussian kernel
 fn gaussian_kernel(radius: f64) -> Vec<f64> {
 	// Given radius, compute the size of the kernel that's approximately three times the radius
@@ -179,3 +209,56 @@ fn box_blur_algorithm(mut original_buffer: Image<Color>, radius: f64, gamma: boo
 
 	y_axis
 }
+
+fn median_filter_algorithm(original_buffer: Image<Color>, radius: u32) -> Image<Color> {
+	let (width, height) = original_buffer.dimensions();
+	let mut output = Image::new(width, height, Color::TRANSPARENT);
+
+	// Pre-allocate and reuse buffers outside the loops to avoid repeated allocations.
+	let window_capacity = ((2 * radius + 1).pow(2)) as usize;
+	let mut r_vals: Vec<f32> = Vec::with_capacity(window_capacity);
+	let mut g_vals: Vec<f32> = Vec::with_capacity(window_capacity);
+	let mut b_vals: Vec<f32> = Vec::with_capacity(window_capacity);
+	let mut a_vals: Vec<f32> = Vec::with_capacity(window_capacity);
+
+	for y in 0..height {
+		for x in 0..width {
+			r_vals.clear();
+			g_vals.clear();
+			b_vals.clear();
+			a_vals.clear();
+
+			// Use saturating_add to avoid potential overflow in extreme cases
+			let y_max = y.saturating_add(radius).min(height - 1);
+			let x_max = x.saturating_add(radius).min(width - 1);
+
+			for ny in y.saturating_sub(radius)..=y_max {
+				for nx in x.saturating_sub(radius)..=x_max {
+					if let Some(px) = original_buffer.get_pixel(nx, ny) {
+						r_vals.push(px.r());
+						g_vals.push(px.g());
+						b_vals.push(px.b());
+						a_vals.push(px.a());
+					}
+				}
+			}
+
+			let r = median_quickselect(&mut r_vals);
+			let g = median_quickselect(&mut g_vals);
+			let b = median_quickselect(&mut b_vals);
+			let a = median_quickselect(&mut a_vals);
+
+			output.set_pixel(x, y, Color::from_rgbaf32_unchecked(r, g, b, a));
+		}
+	}
+
+	output
+}
+/// Finds the median of a slice using quickselect for O(n) average case performance.
+/// This is more efficient than sorting the entire slice which would be O(n log n).
+fn median_quickselect(values: &mut [f32]) -> f32 {
+	let mid: usize = values.len() / 2;
+	// nth_unstable is like quickselect: average O(n)
+	// Use total_cmp for safe NaN handling instead of partial_cmp().unwrap()
+	*values.select_nth_unstable_by(mid, |a, b| a.total_cmp(b)).1
+}