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ARXIV:2603.08018 · IMAGE FUSION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.08018IMAGE FUSIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection.
Opportunity summary
Pain A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection. When the infrared modality is absent, pixel-space…
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. A public repository is linked, so build verification can inspect…
Image Fusion moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection.
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10.48550/arXiv.2603.08018A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection.
Abstract
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently lack interpretability. We address missing-IR fusion by proposing a dictionary-guided, coefficient-domain framework built upon a shared convolutional dictionary. The pipeline comprises three key components: (1) Joint Shared-dictionary Representation Learning (JSRL) learns a unified and interpretable atom space shared by both IR and VIS modalities; (2) VIS-Guided IR Inference (VGII) transfers VIS coefficients to pseudo-IR coefficients in the coefficient domain and performs a one-step closed-loop refinement guided by a frozen large language model as a weak semantic prior; and (3) Adaptive Fusion via Representation Inference (AFRI) merges VIS structures and inferred IR cues at the atom level through window attention and convolutional mixing, followed by reconstruction with the shared dictionary. This encode-transfer-fuse-reconstruct pipeline avoids uncontrolled pixel-space generation while ensuring prior preservation within interpretable dictionary-coefficient representation. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. To our knowledge, this represents the first framework that jointly learns a shared dictionary and performs coefficient-domain inference-fusion to tackle missing-IR fusion. The source code is publicly available at https://github.com/harukiv/DCMIF.
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PROBLEM
A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection. When the infrared modality is absent, pix...
METHOD
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. A public repository is linked, so build verification can inspect imple...
WHY NOW
Image Fusion moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently lack interpretability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently lack interpretability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Fusion moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A dictionary-guided image fusion technique that addresses missing infrared modality by learning a shared convolutional dictionary and performing coefficient-domain inference, enabling improved perceptual quality and downstream detection.
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