ComfyUI_QFT_SRSM

ComfyUI QFT Spectral Residual Saliency Map

A ComfyUI custom node that computes a visual saliency map from a color image using the Quaternion Fourier Transform (QFT) combined with the Spectral Residual (SR) method.

Unlike conventional per-channel approaches, this node encodes all three RGB channels simultaneously as a single pure quaternion, preserving cross-channel color correlations throughout the frequency analysis.

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How It Works

The algorithm follows the QFT-based spectral residual approach described by Schauerte & Stiefelhagen (2012):

1. Pure Quaternion Encoding

Each pixel (R, G, B) is encoded as a pure quaternion:

q(x, y) = R·i + G·j + B·k

The scalar part is zero, and each color channel occupies one of the three imaginary axes.

2. Quaternion Fourier Transform (QFT)

The left-sided QFT with axis μ = i is applied. Using the symplectic decomposition, any pure quaternion q can be split into two complex arrays:

q = p + q_c · j
  where  p   = R·i       (complex: imaginary part = R)
         q_c = G + B·i   (complex: real = G, imaginary = B)

This allows the QFT to be computed via exactly two standard 2-D complex FFTs:

F_L{q}(u, v) = DFT(p)(u, v) + DFT(q_c)(u, v) · j

3. Log Amplitude & Spectral Residual

The quaternion magnitude at each frequency bin is:

A(u, v) = log( sqrt( |DFT(p)|² + |DFT(q_c)|² ) )

A box (average) filter of size smooth_kernel is applied to obtain a smooth "prior" spectrum:

Ā(u, v) = h_n * A(u, v)

The spectral residual is the deviation from this prior:

SR(u, v) = A(u, v) - Ā(u, v)

Visually distinct (salient) regions produce unusually large spectral residuals because they deviate from the repetitive, average structure of the scene.

4. Inverse QFT & Saliency Map

The residual spectrum is reconstructed by applying the weight exp(-Ā) to the original QFT:

F_SR = exp(-Ā) · F_L{q}   =   exp(SR) · unit_quaternion

The inverse QFT is computed via two inverse FFTs, and the saliency at each pixel is the squared quaternion magnitude:

S(x, y) = |IQFT{F_SR}(x, y)|²
         = |IDFT(P_sr)|² + |IDFT(Q_sr)|²

Finally, S is smoothed with a Gaussian filter (gaussian_sigma) and normalized to [0, 1].

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Node Parameters

| Parameter | Type | Default | Range | Description | |-----------|------|---------|-------|-------------| | image | IMAGE | — | — | Input color image (batches supported) | | smooth_kernel | INT | 8 | 1 – 128 | Size of the average filter on the log spectrum. Larger values produce higher contrast between salient and non-salient regions. | | gaussian_sigma | FLOAT | 8.0 | 0.0 – 100.0 | Sigma of the Gaussian blur applied to the final saliency map. Larger values produce smoother, more blob-like saliency regions. | | output_mode | ENUM | grayscale | grayscale / heatmap | grayscale — white-on-black intensity map. heatmap — jet colormap (blue → green → red). |

Output: saliency_map — IMAGE of the same spatial resolution as the input, float32 in [0, 1].

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Installation

1. Clone or copy this folder into your ComfyUI custom_nodes directory:

   git clone <repo-url> /path/to/ComfyUI/custom_nodes/ComfyUI_QFT_SRSM
   

   or copy manually:

   cp -r ComfyUI_QFT_SRSM /path/to/ComfyUI/custom_nodes/
   

2. Install the optional (but recommended) dependency:

   pip install scipy
   

   If scipy is not available, the node automatically falls back to a pure-NumPy implementation of the box and Gaussian filters.

3. Restart ComfyUI. The node will appear under image/analysis as "QFT Spectral Residual Saliency".

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Dependencies

| Package | Required | Notes | |---------|----------|-------| | numpy | Yes | Bundled with ComfyUI | | torch | Yes | Bundled with ComfyUI | | scipy | Recommended | Falls back to NumPy if missing |

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Usage Tips

• smooth_kernel controls how much of the "average" scene statistics are subtracted. A value of 4–16 works well for most images. Very small values (1–2) may produce noisy results; very large values may suppress fine-grained salient details.
• gaussian_sigma should be scaled with image resolution. For a 512×512 image, values of 8–16 are typical starting points.
• The heatmap output mode is useful for visual inspection; connect it directly to a Preview Image node. Use grayscale mode when feeding the saliency map into downstream masking or compositing nodes.
• The node processes each image in the batch independently and supports any resolution.

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References

• Hou, X. & Zhang, L. (2007). Saliency Detection: A Spectral Residual Approach. CVPR 2007.
• Schauerte, B. & Stiefelhagen, R. (2012). Quaternion-based Spectral Saliency Detection for Eye Fixation Prediction. ECCV 2012.
• Ell, T. A. & Sangwine, S. J. (2007). Hypercomplex Fourier Transforms of Color Images. IEEE Transactions on Image Processing.