Spatial Filtering

Convolution-based spatial filters: Gaussian smoothing, gradient magnitude, Canny edge detection, and separable gradient computation.

Design

All spatial filters operate on flat z-major host buffers via substrate-agnostic pure functions. The gradient magnitude and Canny detectors share a common host core that implements gradient computation, non-maximum suppression, and hysteresis. Public apply_native methods preserve the ritk-image::Image geometry boundary while executing those kernels without constructing a second tensor representation.

Gaussian Smoothing

GaussianFilter builds one normalized sampled kernel per axis from physical sigma and voxel spacing, then applies separable zero-padded convolution. The native path and the generic path share the kernel and host-core contracts.

Gradient Magnitude

Computes the magnitude of the gradient via central differences, then optionally smooths with a discrete Gaussian kernel.

Canny Edge Detection

Four stages form the binary edge map:

1. Gaussian smoothing.
2. Gradient magnitude and direction.
3. Non-maximum suppression along the continuous gradient direction.
4. Hysteresis thresholding.

The complete filter gallery uses the real public pipeline on a deterministic phantom and writes the figure below:

Run it from the repository root with:

cargo run -p ritk-filter --example book_filter_gallery -- \
  docs/book/figures/filter_gallery.svg

Verification

The filter example is source-linked from Gaussian Smoothing and Canny Edge Detection. Package tests provide the value-semantic and differential coverage; the figure is a visual smoke check of the same public calls.