Gaussian Blur

Overview

Gaussian blur is applied to FRST and RSD response maps before peak extraction. The blur module provides an in-place implementation with two execution paths selected automatically based on kernel size.

Dual-mode execution

• Direct convolution (sigma <= 2.0): a separable 1D Gaussian kernel with radius $\lceil 3\sigma \rceil$ is applied horizontally then vertically. This is straightforward and efficient for small kernels.

• Stacked box blur (sigma > 2.0): three successive passes of a uniform box filter approximate the Gaussian. Each pass is O(1) per pixel using a sliding-window accumulator, making the total cost independent of sigma. The box widths are chosen so that the cascade converges to the target Gaussian in the limit (Wells 1986; W3C Filter Effects Module Level 1, Section 12.2).

When sigma <= 0.5, the kernel radius would be zero, so the function is a no-op.

Boundary handling

Both paths use mirror-clamp boundary extension: samples outside the image are reflected back from the nearest edge. This avoids dark halos at image borders that would bias peak detection near the edges.

Usage

The blur function is pub(crate) and called internally by the FRST and RSD response computation. It operates in-place on an OwnedImage<f32>, requiring no additional output buffer.