Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement

• URL: http://arxiv.org/abs/2608.05843v1
• Date: Thu, 06 Aug 2026 10:11:36 GMT
• Title: Overcoming Attention Drift: Homogeneity-Heterogeneity Guided Feature Aggregation for Low-Light Remote Sensing Image Enhancement
• Abstract summary: Under severe noise and illumination corruption, existing methods suffer from attention drift and severe structural blurring and color distortion.<n>We propose HALO, a dual-prior-driven enhancement framework that formulates enhancement as a guided feature aggregation problem driven by foundation model priors.<n>H2CAM achieves state-of-the-art performance across 8 challenging synthetic and real-world remote sensing benchmarks.
• Score: 10.412281080988647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Restoring high-fidelity remote sensing imagery from extreme low-light degradation is indispensable for reliable Earth observation and downstream machine vision. However, under severe noise and illumination corruption, existing methods suffer from attention drift, erroneously aggregating features across distinct physical boundaries and causing severe structural blurring and color distortion. To address this, we propose HALO, a dual-prior-driven enhancement framework that formulates enhancement as a guided feature aggregation problem driven by foundation model priors. Specifically, an illumination-invariant semantic prior provides regional homogeneity as a positive bias for content-consistent aggregation, while a pseudo-3D topological prior provides boundary heterogeneity as a negative penalty to strictly prevent cross-boundary confusion. To cooperatively incorporate these two priors, we propose a Homogeneity-Heterogeneity Cooperative Attention Module (H2CAM) to resolve feature conflicts during cross-modal prior fusion. Extensive experiments demonstrate that HALO achieves state-of-the-art performance across 8 challenging synthetic and real-world remote sensing benchmarks, significantly improving physical boundary sharpness and color fidelity while maximizing the preservation of discriminative features for downstream Earth observation tasks.

Related papers

• Beyond Unfolding: 60x Faster One-Stage Unmixing for Closely-Spaced Infrared Small Targets [43.71958109614362]
  Closely-Spaced Infrared Small Targets (CSIST) typically exhibit energy overlap, manifesting as indistinguishable blobs in infrared images.<n>This ambiguity invalidates the one-to-one mapping assumption of traditional detection.<n>We propose a one-stage lightweight paradigm which demonstrates that deep unfolding is not necessary.
  arXiv  Detail & Related papers  (2026-07-17T14:42:51Z)
• Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement [18.972830666871005]
  Low-Light Image Enhancement (LLIE) requires a careful balance among noise suppression, color fidelity, and efficiency.<n>Recent HVI-based methods alleviate color entanglement by decoupling intensity and chromaticity, yet how reliably the two streams are fused again is an overlooked factor that largely determines the final quality.<n>We propose TCA-Net, a network built around Thresholded Cross-Attention that targets reliable intensity-chromaticity fusion in the HVI space.
  arXiv  Detail & Related papers  (2026-07-15T15:06:16Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization [82.42818558196615]
  In-the-wild 3D Gaussian Splatting remains challenging due to transient distractors and illumination-induced cross-view appearance inconsistencies.<n>We propose a conflict-aware 3DGS framework that addresses this problem from both image-space supervision and gradient-level optimization.<n>Our method achieves state-of-the-art rendering quality under complex transient distractors and cross-view inconsistencies.
  arXiv  Detail & Related papers  (2026-05-13T06:47:09Z)
• Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder [34.73963627819185]
  High-resolution (HR) joint optical and synthetic aperture radar (SAR) pretraining seeks modality synergy to mutually enhance single-source representations.<n>We propose CoDe-MAE, pioneering a textitbetter synergy with less alignment philosophy.<n>CoDe-MAE successfully prevents representation degradation and establishes new state-of-the-art performance across diverse single- and bi-modal downstream tasks.
  arXiv  Detail & Related papers  (2026-04-18T10:23:00Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• Hyperspectral Image Generation with Unmixing Guided Diffusion Model [21.078386095749398]
  Hyperspectral image synthesis is a problem that has garnered growing interest yet remains constrained by the conditional generative paradigms that limit sample diversity.<n>We introduce a diffusion framework explicitly guided by hyperspectral unmixing to overcome the challenges.<n>The approach integrates two collaborative components: (i) an unmixing autoencoder that projects generation from the image domain into a low-dimensional abundance manifold, and (ii) an abundance diffusion process that enforces non-negativity and sum-to-one constraints.
  arXiv  Detail & Related papers  (2025-06-03T08:27:10Z)
• Learning to Align and Refine: A Foundation-to-Diffusion Framework for Occlusion-Robust Two-Hand Reconstruction [50.952228546326516]
  Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures.<n>Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts.<n>We propose a dual-stage Foundation-to-Diffusion framework that precisely align 2D prior guidance from vision foundation models.
  arXiv  Detail & Related papers  (2025-03-22T14:42:27Z)
• D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction [74.49121940466675]
  We introduce centroid-fixed dual-stream conditional diffusion for monocular hand-held object reconstruction. First, to avoid the object centroid from deviating, we utilize a novel hand-constrained centroid fixing paradigm. Second, we introduce a dual-stream denoiser to semantically and geometrically model hand-object interactions.
  arXiv  Detail & Related papers  (2023-11-23T20:14:50Z)
• Heterogeneous Face Frontalization via Domain Agnostic Learning [74.86585699909459]
  We propose a domain agnostic learning-based generative adversarial network (DAL-GAN) which can synthesize frontal views in the visible domain from thermal faces with pose variations. DAL-GAN consists of a generator with an auxiliary classifier and two discriminators which capture both local and global texture discriminations for better synthesis.
  arXiv  Detail & Related papers  (2021-07-17T20:41:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.