diff --git a/src/ImageSharp/Processing/Processors/Convolution/ConvolutionProcessorHelpers.cs b/src/ImageSharp/Processing/Processors/Convolution/ConvolutionProcessorHelpers.cs
index ebda2eb079..37adaf5402 100644
--- a/src/ImageSharp/Processing/Processors/Convolution/ConvolutionProcessorHelpers.cs
+++ b/src/ImageSharp/Processing/Processors/Convolution/ConvolutionProcessorHelpers.cs
@@ -9,6 +9,7 @@ internal static class ConvolutionProcessorHelpers
     /// Kernel radius is calculated using the minimum viable value.
     /// See <see href="http://chemaguerra.com/gaussian-filter-radius/"/>.
     /// </summary>
+    /// <param name="sigma">The weight of the blur.</param>
     internal static int GetDefaultGaussianRadius(float sigma)
         => (int)MathF.Ceiling(sigma * 3);
 
@@ -16,9 +17,11 @@ internal static class ConvolutionProcessorHelpers
     /// Create a 1 dimensional Gaussian kernel using the Gaussian G(x) function.
     /// </summary>
     /// <returns>The convolution kernel.</returns>
+    /// <param name="size">The kernel size.</param>
+    /// <param name="weight">The weight of the blur.</param>
     internal static float[] CreateGaussianBlurKernel(int size, float weight)
     {
-        var kernel = new float[size];
+        float[] kernel = new float[size];
 
         float sum = 0F;
         float midpoint = (size - 1) / 2F;
@@ -44,9 +47,11 @@ internal static class ConvolutionProcessorHelpers
     /// Create a 1 dimensional Gaussian kernel using the Gaussian G(x) function
     /// </summary>
     /// <returns>The convolution kernel.</returns>
+    /// <param name="size">The kernel size.</param>
+    /// <param name="weight">The weight of the blur.</param>
     internal static float[] CreateGaussianSharpenKernel(int size, float weight)
     {
-        var kernel = new float[size];
+        float[] kernel = new float[size];
 
         float sum = 0;
 
diff --git a/tests/ImageSharp.Tests/Processing/Processors/Convolution/ConvolutionProcessorHelpersTest.cs b/tests/ImageSharp.Tests/Processing/Processors/Convolution/ConvolutionProcessorHelpersTest.cs
index 740bdddafd..574c983710 100644
--- a/tests/ImageSharp.Tests/Processing/Processors/Convolution/ConvolutionProcessorHelpersTest.cs
+++ b/tests/ImageSharp.Tests/Processing/Processors/Convolution/ConvolutionProcessorHelpersTest.cs
@@ -1,73 +1,70 @@
 // Copyright (c) Six Labors.
 // Licensed under the Six Labors Split License.
 
-using System;
 using SixLabors.ImageSharp.Processing.Processors.Convolution;
-using Xunit;
 
-namespace SixLabors.ImageSharp.Tests.Processing.Processors.Convolution
+namespace SixLabors.ImageSharp.Tests.Processing.Processors.Convolution;
+
+[GroupOutput("Convolution")]
+public class ConvolutionProcessorHelpersTest
 {
-    [GroupOutput("Convolution")]
-    public class ConvolutionProcessorHelpersTest
+    [Theory]
+    [InlineData(3)]
+    [InlineData(5)]
+    [InlineData(9)]
+    [InlineData(22)]
+    [InlineData(33)]
+    [InlineData(80)]
+    public void VerifyGaussianKernelDecomposition(int radius)
     {
-        [Theory]
-        [InlineData(3)]
-        [InlineData(5)]
-        [InlineData(9)]
-        [InlineData(22)]
-        [InlineData(33)]
-        [InlineData(80)]
-        public void VerifyGaussianKernelDecomposition(int radius)
-        {
-            int kernelSize = (radius * 2) + 1;
-            float sigma = radius / 3F;
-            float[] kernel = ConvolutionProcessorHelpers.CreateGaussianBlurKernel(kernelSize, sigma);
-            DenseMatrix<float> matrix = DotProduct(kernel, kernel);
+        int kernelSize = (radius * 2) + 1;
+        float sigma = radius / 3F;
+        float[] kernel = ConvolutionProcessorHelpers.CreateGaussianBlurKernel(kernelSize, sigma);
+        DenseMatrix<float> matrix = DotProduct(kernel, kernel);
 
-            bool result = matrix.TryGetLinearlySeparableComponents(out float[] row, out float[] column);
+        bool result = matrix.TryGetLinearlySeparableComponents(out float[] row, out float[] column);
 
-            Assert.True(result);
-            Assert.NotNull(row);
-            Assert.NotNull(column);
-            Assert.Equal(row.Length, matrix.Rows);
-            Assert.Equal(column.Length, matrix.Columns);
+        Assert.True(result);
+        Assert.NotNull(row);
+        Assert.NotNull(column);
+        Assert.Equal(row.Length, matrix.Rows);
+        Assert.Equal(column.Length, matrix.Columns);
 
-            float[,] dotProduct = DotProduct(row, column);
+        float[,] dotProduct = DotProduct(row, column);
 
-            for (int y = 0; y < column.Length; y++)
+        for (int y = 0; y < column.Length; y++)
+        {
+            for (int x = 0; x < row.Length; x++)
             {
-                for (int x = 0; x < row.Length; x++)
-                {
-                    Assert.True(Math.Abs(matrix[y, x] - dotProduct[y, x]) < 0.0001F);
-                }
+                Assert.True(Math.Abs(matrix[y, x] - dotProduct[y, x]) < 0.0001F);
             }
         }
+    }
 
-        [Fact]
-        public void VerifyNonSeparableMatrix()
-        {
-            bool result = LaplacianKernels.LaplacianOfGaussianXY.TryGetLinearlySeparableComponents(
-                out float[] row,
-                out float[] column);
+    [Fact]
+    public void VerifyNonSeparableMatrix()
+    {
+        bool result = LaplacianKernels.LaplacianOfGaussianXY.TryGetLinearlySeparableComponents(
+            out float[] row,
+            out float[] column);
 
-            Assert.False(result);
-            Assert.Null(row);
-            Assert.Null(column);
-        }
+        Assert.False(result);
+        Assert.Null(row);
+        Assert.Null(column);
+    }
 
-        private static DenseMatrix<float> DotProduct(float[] row, float[] column)
-        {
-            float[,] matrix = new float[column.Length, row.Length];
+    private static DenseMatrix<float> DotProduct(float[] row, float[] column)
+    {
+        float[,] matrix = new float[column.Length, row.Length];
 
-            for (int x = 0; x < row.Length; x++)
+        for (int x = 0; x < row.Length; x++)
+        {
+            for (int y = 0; y < column.Length; y++)
             {
-                for (int y = 0; y < column.Length; y++)
-                {
-                    matrix[y, x] = row[x] * column[y];
-                }
+                matrix[y, x] = row[x] * column[y];
             }
-
-            return matrix;
         }
+
+        return matrix;
     }
 }