Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation

Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/unified-restoration-perception-learning-maritime-infrared-visible-image-fusion-and-segmentation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 45
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID unified-restoration-perception-learning-maritime-infrared-visible-image-fusion-and-segmentation | Route /signal-canvas/unified-restoration-perception-learning-maritime-infrared-visible-image-fusion-and-segmentation

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/unified-restoration-perception-learning-maritime-infrared-visible-image-fusion-and-segmentation

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 45

Proof: Verification pending

Freshness state: computing

Source paper: Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation

PDF: https://arxiv.org/pdf/2603.28414v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:19:49.640Z

Signal Canvas receipt window

Watch and verify: Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation

/buildability/unified-restoration-perception-learning-maritime-infrared-visible-image-fusion-and-segmentation

Watchwatch

Subject: Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

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GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
IVMD fills the gap of lacking real maritime multi-degradation datasets.
Implicationpartial
Explicitly stated as a key contribution in the parsed text.
Verificationpartial
partial
Evidencepartial
This framework bridges the gap between low-level degradation restoration and high-level semantic perception, achieving collaborative optimization of the two tasks.
Implicationpartial
Directly stated as a framework innovation in the parsed text.
Verificationpartial
partial
Evidencepartial
the Frequency-Spatial Enhancement Complementary (FSEC) module suppresses multi-type degradations via frequency
Implicationpartial
Directly stated in the module design description.
Verificationpartial
partial
Evidencepartial
These unified frameworks have shown promise in handling heterogeneous degradations, but they focus primarily on enhancing the visual quality of the restored images, while overlooking the impact of the restoration results on the performance of downstream vision tasks.
Implicationpartial
Directly stated as a limitation of prior work in the parsed text.
Verificationpartial
partial
Evidencepartial
Experimental results on IVMSD demonstrate that the proposed method achieves state-of-the-art segmentation performance, significantly enhancing robustness and perceptual quality under complex maritime conditions.
Implicationpartial
Explicitly stated in the abstract with supporting experimental results implied.
Verificationpartial
partial
Evidencepartial
Moreover, publicly available infrared-visible datasets are predominantly collected from urban scenes, failing to capture the authentic characteristics of coupled degradations in marine environments.
Implicationpartial
Directly stated in the abstract as a motivation for creating the new dataset.
Verificationpartial
partial
Evidencepartial
This module leverages the discrete wavelet transform (DWT) for multi-resolution decomposition... The Fast Fourier Transform (FFT)-based filtering performs global frequency refinement
Implicationpartial
Technical details are explicitly described in the method section.
Verificationpartial
partial
Evidencepartial
this paper ultimately obtained a dataset containing 2,313 rigorously aligned high-quality infrared-visible i
Implicationpartial
Specific numeric count is provided in the dataset description.
Verificationpartial
partial

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Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation

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