IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal

• URL: http://arxiv.org/abs/2606.09347v2
• Date: Wed, 10 Jun 2026 00:14:49 GMT
• Title: IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal
• Abstract summary: We propose an Information Bottleneck-driven High-Fidelity Network (IB-HFN) for SAR-assisted optical cloud removal.<n>IB-HFN employs a dual-stream backbone to preserve modality-specific representations before deep semantic fusion.<n> Experiments on the SEN12MS-CR dataset under challenging-temporal splits demonstrate that IB-HFN achieves superior structural preservation and spectral fidelity over existing methods.
• Score: 18.935012033334477
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images by exploiting complementary SAR observations. Existing multimodal fusion methods typically rely on direct spatial concatenation and pixel-wise supervision, which can propagate SAR speckle noise into optical reconstruction and lead to over-smoothed results. To address these limitations, we propose an Information Bottleneck-driven High-Fidelity Network (IB-HFN) for SAR-assisted optical cloud removal. IB-HFN employs a dual-stream backbone to preserve modality-specific representations before deep semantic fusion, thereby mitigating premature cross-modal contamination. At the fusion stage, we introduce a Spatial Information Bottleneck Fusion module that compresses SAR features through a channel-wise variational information bottleneck to suppress unstructured speckle noise. In parallel, a local-global gating mechanism predicts clear-sky regions and routes reliable optical details through a Dirac-initialized skip connection, decoupling noise suppression from texture preservation. We further develop a joint optimization strategy that integrates feature-level bottleneck regularization with image-level constraints on reconstruction accuracy, structural consistency, spectral fidelity, and contrastive sharpness. A dynamic weighting schedule balances these objectives to stabilize training and reduce hazy artifacts. Experiments on the SEN12MS-CR dataset under challenging spatio-temporal splits demonstrate that IB-HFN achieves superior structural preservation and spectral fidelity over existing methods.

Related papers

• Making Every Step Count: Spatio-Temporal Information Allocation for Imaging Inverse Problems [63.904793414062816]
  We introduce two complementary and training-free components, i.e., Spectrum-based Scheduling (SAS) and Measurement-Prioritized Attention (MPA)<n>SAS distributes the available NFEs over flow time according to the spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement.<n>MPA guides information toward weakly constrained regions, thereby enhancing semantic and structural fidelity.
  arXiv  Detail & Related papers  (2026-08-12T07:39:22Z)
• Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset [8.89851770832353]
  CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion framework.<n>It directly predicts LULC maps from cloud-contaminated Sentinel-2 imagery and temporally adjacent Sentinel-1 SAR observations.<n>Experiments show that CloudLULC-Net achieves an OA of 86.60%, an F1-score of 83.29%, and an mIoU of 73.51%.
  arXiv  Detail & Related papers  (2026-06-16T09:25:10Z)
• HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation [50.46910397782266]
  Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss.<n>We present HIR-ALIGN, a plug-and-play target-adaptive augmentation framework that enhances hyperspectral image restoration by augmenting limited training images with synthetic data that closely matches the target distribution using no extra data.
  arXiv  Detail & Related papers  (2026-05-13T14:14:13Z)
• Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception [6.402472465609055]
  High-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission.<n>We propose the Structure-Aware Latent Diffusion (SALD) framework, an asymmetric edge-cloud collaborative SR system.<n>Under extreme bandwidth constraints, SALD achieves superior perceptual quality (LPIPS) and significantly enhances downstream performance in both scene classification and small-target detection.
  arXiv  Detail & Related papers  (2026-04-28T07:28:39Z)
• ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing [10.634107398163676]
  Single image dehazing is often constrained by a trade-off between restoration quality and computational efficiency.<n>ZID-Net is a novel framework that explicitly decouples diffusion supervision from feed-forward inference.<n>ZID-Net achieves 40.75 dB PSNR on the synthetic RESIDE dataset and outperforms existing methods with a 1.13 dB gain on real-world datasets.
  arXiv  Detail & Related papers  (2026-04-26T13:44:25Z)
• Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation [11.468907022707013]
  TDP-CR is a task-driven framework that jointly performs cloud removal and land-cover segmentation.<n>Central to our approach is a Prompt-Guided Fusion mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty.<n>Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework.
  arXiv  Detail & Related papers  (2026-01-17T13:32:38Z)
• Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement [89.99237142387655]
  We introduce LH-VAE, which enhances semantic robustness through visual semantic constraints and progressive degradations.<n>Latent Harmony is a two-stage framework that redefines VAEs for UHD restoration by jointly regularizing the latent space and enforcing high-frequency-aware reconstruction.<n>Experiments show Latent Harmony achieves state-of-the-art performance across UHD and standard-resolution tasks, effectively balancing efficiency, perceptual quality, and reconstruction accuracy.
  arXiv  Detail & Related papers  (2025-10-09T08:54:26Z)
• Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing [92.61216319417208]
  We propose a novel frequency domain-based diffusion model, named ours, for fully exploiting the beneficial knowledge in unpaired clear data.<n>Inspired by the strong generative ability shown by Diffusion Models (DMs), we tackle the dehazing task from the perspective of frequency domain reconstruction.
  arXiv  Detail & Related papers  (2025-07-02T01:22:46Z)
• Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions [0.0]
  Cloud contamination significantly impairs the usability of optical satellite imagery.<n>This research presents a Cloud-Attentive Reconstruction Framework that integrates SAR-optical feature fusion with deep learning-based image reconstruction.
  arXiv  Detail & Related papers  (2025-06-22T03:27:41Z)
• DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model [27.83437788159158]
  We introduce DehazeMamba, a novel SAR-guided dehazing network built on a progressive haze decoupling fusion strategy.<n>Our approach incorporates two key innovations: a Haze Perception and Decoupling Module (HPDM) that dynamically identifies haze-affected regions through optical-SAR difference analysis, and a Progressive Fusion Module (PFM) that mitigates domain shift through a two-stage fusion process based on feature quality assessment.<n>Extensive experiments demonstrate that DehazeMamba significantly outperforms state-of-the-art methods, achieving a 0.73 dB improvement in PSNR and substantial enhancements in downstream tasks such as
  arXiv  Detail & Related papers  (2025-03-17T11:25:05Z)
• SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models [52.40011613324083]
  Joint source-channel coding systems (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission.<n>Existing methods focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality.<n>We propose SING, a novel framework that formulates the recovery of high-quality images from corrupted reconstructions as an inverse problem.
  arXiv  Detail & Related papers  (2025-03-16T12:32:11Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)

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.