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03/10/2026

Lightweight Attention Fusion with Receptive-Field Enhancement for Visible–Infrared Images

Image fusion

Image fusion combines two or more images of the same scene into one image that keeps the useful content of each. The inputs are often different modalities or different settings: a visible image and an infrared image, two exposures, or a sharp panchromatic image and a lower-resolution spectral image. The output should retain thermal targets from the infrared frame and textures from the visible frame, without inventing edges. A fusion result is judged by whether a later task, such as detection, works better on the fused image than on either input alone, and by whether the fused image is free of halos and washed-out contrast.

Attention mechanism

An attention mechanism assigns a weight to each position, channel, or token so the network emphasizes some features and suppresses others. In fusion, spatial attention can highlight a pedestrian that is bright in infrared and dim in visible light. Channel attention can prefer the infrared channels in a dark region and the visible channels in a textured region. The weights are computed from the features themselves, then multiplied back onto those features. Attention does not add new measurements. It reallocates the ones the encoders already produced.

Receptive field enhancement

The receptive field of a unit is the region of the input image that can affect that unit. A \(3\times 3\) convolution sees only a small neighborhood. Fusion needs both that local detail and a wider context, because a bright infrared region should be interpreted against the surrounding scene. Enhancement enlarges the field without a matching explosion of cost: dilated convolutions, a small multi-scale branch, or a lightweight pooling path. Too large a field blurs fine edges. The useful field is the smallest one that still lets the fusion rule use scene context.

Lightweight network

A lightweight network reaches usable accuracy with few parameters, few operations, and a small memory footprint, so it can run on an edge device or a camera processor. Depthwise separable convolutions, shared weights, and narrow channels are the usual means. In fusion the constraint is hard because two encoders and one decoder must all fit. A lightweight design is successful only if the fused image still holds the targets and the textures that the heavy model held, at a stated latency and a stated power.

How they fit

Two images enter a small network. Attention decides, at each place, which source to trust. An enlarged receptive field makes that decision with context rather than with a single pixel. The lightweight constraint keeps the whole path deployable. The check is task performance on the fused image, artifact rate, and cost on the target device.
Strong Relevant Logic, United Vision, Success History Multi-Agent Pathfinding, Distributed Computing. Response Systems Common Sense Management Jordan James Etem

03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Strong Relevant Logic, United Vision, Success History Multi-Agent Pathfinding, Distributed Computing. Response Systems Common Sense Management Jordan James Etem

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03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Spain Business Hub Strong Relevant Logic, United Vision, Success History

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03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Strong Relevant Logic, United Vision, Success History

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03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Strong Relevant Logic, United Vision, Success History

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03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Strong Relevant Logic, United Vision, Success History

03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] Strong Relevant Logic, United Vision, Success History Multi-Agent Pathfinding, Distributed Computing. Response Systems Common Sense Management Jordan James Etem

03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}] NVIDIA Robotics Attitudes as Networks, Inspiring, Positive Affect for Achievement

03/10/2026

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03/10/2026

axiter = 20; nn = 10000; alfa = 2; bas = Sum[1/(k*Log[k]^alfa), {k, 2, nn}] + 1/((alfa - 1)*Log[nn + 1/2]^(alfa - 1)); sub = 0; Do[sub = sub + 1/4^s/(2*s + 1)! * NSum[(D[1/(x*Log[x]^alfa), {x, 2 s}]) /. x -> k, {k, nn + 1, Infinity}, WorkingPrecision -> 120, NSumTerms -> 100000, PrecisionGoal -> 120, Method -> {"NIntegrate", "MaxRecursion" -> 100}]; Print[bas - sub], {s, 1, maxiter}]
Strong Relevant Logic, United Vision, Success History
Multi-Agent Pathfinding, Distributed Computing. Response Systems
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