Focus Peeking

Focus Peeking: The Laplacian of Gaussian Method

The creation of the Focus Mask is executed by the GPU on your Mac, resulting in a relatively swift process, even when the mask is generated on the fly.

Note: To obtain the most optimal results in Focus Peek view (extract), it is recommended to extract the JPG file from the ARW file. While it is possible to view Focus Peek by creating thumbnails, the mask is more effective when applied to JPGs. To access Focus Peek, double-click on the file table row. In the Settings menu, Thumbnails tab, disable the “Use Thumbnail for Zoom” to use JPGs as base for Focus Peek.

What Is Focus Peeking?

Focus peeking is a visual aid that highlights which areas of a photograph are in sharp focus. Rather than relying on your eye alone to judge sharpness, focus peeking overlays a red highlight on pixels where edge detail and contrast are strongest. Those highlighted pixels are the ones in sharpest focus — everything else stays unchanged.

Why the Laplacian of Gaussian?

Several mathematical approaches can measure local sharpness, but the Laplacian of Gaussian (LoG) is one of the most effective for focus peeking because it combines two complementary operations.

The Gaussian blur is applied first, suppressing sensor noise and low-amplitude out-of-focus texture — water ripples, skin, smooth backgrounds — before sharpness detection runs. Without this step, the Laplacian would flag noise and soft texture as “sharp.”

The Laplacian then computes, for each pixel, how much its intensity differs from its immediate neighbours. In a blurry region those differences are small and smooth. At a sharply rendered edge they spike. The key insight is that the Laplacian measures second-order intensity change — it responds to genuine focus-induced contrast spikes rather than gradual gradients, which blurred regions produce even when they have visible texture.

The Kernel

Rather than the classic 4-neighbour approximation, RawCull uses a full 8-neighbour kernel computed on the GPU via Metal:

L = 8 × center − (N + S + E + W + NE + NW + SE + SW)

This 3×3 formulation is more isotropic than the 4-neighbour version — it responds equally to edges in all directions, including diagonals, which matters for natural subjects like feathers and fur.

The sharpness energy at each pixel is then computed as a luminance-weighted magnitude:

energy = |L.r| × 0.299 + |L.g| × 0.587 + |L.b| × 0.114

This preserves perceptual weighting — the green channel contributes most, matching human sensitivity to luminance detail.

Step-by-Step: How It Works

1. Downscale. The image is reduced to 1024 pixels wide before processing. The algorithm operates on pixel neighbourhoods, so full resolution is unnecessary and the performance gain is significant.

2. Pre-blur (Gaussian). A Gaussian blur is applied to the downscaled image. This suppresses out-of-focus texture and sensor noise before the Laplacian runs, preventing false highlights in blurred regions. The blur radius is user-adjustable — higher values ignore more background texture.

3. Laplacian kernel (Metal GPU). The 8-neighbour kernel runs on every pixel in parallel on the GPU, computing the second-order derivative of brightness across all channels.

4. Energy and amplification. The per-pixel result is converted to a single luminance-weighted sharpness energy value and amplified. The amplification factor is user-adjustable.

5. Threshold. Any pixel below the threshold is discarded (black). Only genuinely sharp pixels survive. The threshold is the primary control — lower values highlight more of the image, higher values restrict highlights to only the sharpest edges.

6. Dilation. Surviving pixels are slightly expanded morphologically, turning scattered dots into solid readable regions without introducing false edges.

7. Red overlay. Surviving pixels are colourised red and composited over the original photograph using a screen blend mode. Black (non-sharp) areas are transparent in screen blending, so only the red sharpness regions show through over the photo.

Tunable Parameters

RawCull exposes four controls in the Focus Mask panel:

A Reset button returns all values to their defaults.

Practical Considerations

Threshold tuning is the most important variable. Too low and nearly the entire image lights up. Too high and only the absolute sharpest micro-contrast registers. A good starting point is around 0.25–0.35, then adjusted for the scene.

Pre-blur is your noise lever. Scenes with prominent out-of-focus texture — water, foliage backgrounds, bokeh — benefit from a higher pre-blur radius. Scenes with a small sharp subject against a smooth background can use a lower value.

Subject texture matters. Smooth, low-contrast surfaces — clear sky, still water, skin — produce a weak Laplacian response even when perfectly focused, because there is little edge detail to detect. Focus peeking works best on subjects with natural texture: feathers, fur, foliage, fabric.

RAW vs JPG. The mask is more effective when applied to extracted JPGs rather than thumbnails. Thumbnails are heavily processed and downscaled before RawCull sees them, which reduces the fine edge detail the Laplacian depends on.

Summary

RawCull’s focus peeking runs a Laplacian of Gaussian pipeline entirely on the GPU via Metal. A Gaussian pre-blur suppresses noise and out-of-focus texture; an 8-neighbour Laplacian kernel detects genuine sharpness spikes; thresholding and morphological dilation produce a clean binary mask; and screen blending composites the red overlay non-destructively over the original image. The result is a fast, accurate, and visually interpretable sharpness map that makes focus assessment immediate — particularly valuable when reviewing large numbers of action or wildlife shots where critical focus on a small moving subject is difficult to judge from the image alone.