diff --git a/src/filter/mod.rs b/src/filter/mod.rs
index 97f9c8de..7a9c6a77 100644
--- a/src/filter/mod.rs
+++ b/src/filter/mod.rs
@@ -59,27 +59,6 @@ pub fn bilateral_filter(
         (-0.5 * x.powi(2) / sigma_squared).exp()
     }
 
-    /// Effectively a meshgrid command with flattened outputs.
-    fn window_coords(window_size: u32) -> (Vec<i32>, Vec<i32>) {
-        let window_start = (-(window_size as f32) / 2.0).floor() as i32;
-        let window_end = (window_size as f32 / 2.0).floor() as i32 + 1;
-        let window_range = window_start..window_end;
-
-        let cc = window_range
-            .clone()
-            .cycle()
-            .take(window_range.len().pow(2))
-            .collect();
-
-        let n = window_size as usize + 1;
-        let mut rr = Vec::with_capacity(n * window_range.len());
-        for i in window_range {
-            rr.extend(std::iter::repeat(i).take(n));
-        }
-
-        (rr, cc)
-    }
-
     /// Create look-up table of Gaussian weights for color dimension.
     fn compute_color_lut(bins: u32, sigma: f32, max_value: f32) -> Vec<f32> {
         let step_size = max_value / bins as f32;
@@ -90,14 +69,22 @@ pub fn bilateral_filter(
 
     /// Create look-up table of weights corresponding to flattened 2-D Gaussian kernel.
     fn compute_spatial_lut(window_size: u32, sigma: f32) -> Vec<f32> {
-        let (rr, cc) = window_coords(window_size);
-        let it = rr.into_iter().zip(cc);
+        let window_start = (-(window_size as f32) / 2.0).floor() as i32;
+        let window_end = (window_size as f32 / 2.0).floor() as i32 + 1;
+        let window_range = window_start..window_end;
+
+        let cc = window_range.clone().cycle().take(window_range.len().pow(2));
+
+        let n = window_size as usize + 1;
+        let rr = window_range.flat_map(|i| std::iter::repeat(i).take(n));
+
         let sigma_squared = sigma.powi(2);
-        it.map(|(r, c)| {
-            let dist = ((r as f32).powi(2) + (c as f32).powi(2)).sqrt();
-            gaussian_weight(dist, sigma_squared)
-        })
-        .collect()
+        rr.zip(cc)
+            .map(|(r, c)| {
+                let dist = ((r as f32).powi(2) + (c as f32).powi(2)).sqrt();
+                gaussian_weight(dist, sigma_squared)
+            })
+            .collect()
     }
 
     let max_value = *image.iter().max().unwrap() as f32;
@@ -122,13 +109,10 @@ pub fn bilateral_filter(
                 let window_col_abs = (col as i32 + window_col).clamp(0, width as i32 - 1); // Wrap to edge.
                 let kc = window_col + window_extent;
                 let range_bin = (kr * window_size + kc) as usize;
-                let range_weight = range_lut[range_bin];
                 let val = image.get_pixel(window_col_abs as u32, window_row_abs as u32)[0];
-                let color_dist = (window_center_val - val as i32).abs();
-                let color_bin = (color_dist as f32 * color_dist_scale) as usize;
-                let color_bin = min(color_bin, max_color_bin);
-                let color_weight = color_lut[color_bin];
-                let weight = range_weight * color_weight;
+                let color_dist = (window_center_val - val as i32).abs() as f32;
+                let color_bin = ((color_dist * color_dist_scale) as usize).min(max_color_bin);
+                let weight = range_lut[range_bin] * color_lut[color_bin];
                 total_val += val as f32 * weight;
                 total_weight += weight;
             }