Performing Convolution in SkiaSharp
Convolution powers many imaging effects, but naive implementations slow down badly as kernel size grows. David Britch shows how SkiaSharp’s built-in matrix convolution filter delivers arbitrary-size kernels at speed in a .NET MAUI app.
What you’ll learn
- What convolution is — weighting each pixel by its neighbours using a kernel matrix for effects like blur, sharpen, emboss, and edge detection
- Defining kernels — a
ConvolutionKernelsclass with edge detection, Laplacian of Gaussian, and emboss examples - Why the built-in filter wins — avoiding the O(N²) cost of a hand-written NxN loop
- Using CreateMatrixConvolution — configuring kernel size, scale, bias, offset, tile mode, and alpha handling on an
SKImageFilter - Rendering the result — applying the filter via
SKPaintand snapshotting the surface for further operations
Read the full post for the kernel definitions, the full convolution method, and result screenshots.