Real-World Remote Sensing Image Dehazing: Benchmark and Baseline

• URL: http://arxiv.org/abs/2503.17966v1
• Date: Sun, 23 Mar 2025 07:15:46 GMT
• Title: Real-World Remote Sensing Image Dehazing: Benchmark and Baseline
• Authors: Zeng-Hui Zhu, Wei Lu, Si-Bao Chen, Chris H. Q. Ding, Jin Tang, Bin Luo,
• Abstract summary: The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets.<n>We introduce Real-World Remote Sensing Hazy Image dataset (RRSHID), the first large-scale dataset featuring real-world hazy and dehazed image pairs.<n>Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID.
• Score: 19.747354924759104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with real-world applications due to the inherent domain gap between synthetic and real data. To address this, we introduce Real-World Remote Sensing Hazy Image Dataset (RRSHID), the first large-scale dataset featuring real-world hazy and dehazed image pairs across diverse atmospheric conditions. Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID. Its effectiveness arises from three innovative components: Multi-branch Feature Integration Block Aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multi-branch processing; Color-Calibrated Self-Supervised Attention Module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and Multi-Scale Feature Adaptive Fusion Module (MFAFM), which integrates features effectively while preserving local details and global context. Extensive experiments validate that MCAF-Net demonstrates state-of-the-art performance in real-world RSID, while maintaining competitive performance on synthetic datasets. The introduction of RRSHID and MCAF-Net sets new benchmarks for real-world RSID research, advancing practical solutions for this complex task. The code and dataset are publicly available at \url{https://github.com/lwCVer/RRSHID}.

Related papers

• An Efficient and Mixed Heterogeneous Model for Image Restoration [71.85124734060665]
  Current mainstream approaches are based on three architectural paradigms: CNNs, Transformers, and Mambas. We propose RestorMixer, an efficient and general-purpose IR model based on mixed-architecture fusion.
  arXiv  Detail & Related papers  (2025-04-15T08:19:12Z)
• Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes [51.92255321684027]
  We propose a novel plug-and-play module designed to mitigate misalignment issues by aligning LR inputs with HR images during training. Specifically, our approach involves mimicking a novel LR sample that aligns with HR while preserving the characteristics of the original LR samples. We comprehensively evaluate our method on synthetic and real-world datasets, demonstrating its effectiveness across a spectrum of SR models.
  arXiv  Detail & Related papers  (2024-10-07T18:18:54Z)
• WTCL-Dehaze: Rethinking Real-world Image Dehazing via Wavelet Transform and Contrastive Learning [17.129068060454255]
  Single image dehazing is essential for applications such as autonomous driving and surveillance. We propose an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform. Our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods.
  arXiv  Detail & Related papers  (2024-10-07T05:36:11Z)
• Towards Realistic Data Generation for Real-World Super-Resolution [58.99206459754721]
  RealDGen is an unsupervised learning data generation framework designed for real-world super-resolution.<n>We develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model.<n>Experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations.
  arXiv  Detail & Related papers  (2024-06-11T13:34:57Z)
• IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model [7.842507196763463]
  IRSRMamba is a novel framework integrating wavelet transform feature modulation for multi-scale adaptation.<n>IRSRMamba outperforms state-of-the-art methods in PSNR, SSIM, and perceptual quality.<n>This work establishes Mamba-based architectures as a promising direction for high-fidelity IR image enhancement.
  arXiv  Detail & Related papers  (2024-05-16T07:49:24Z)
• RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications [55.24463002889]
  We focus on depth data synthesis and develop a range-aware RGB-D data simulation pipeline (RaSim) In particular, high-fidelity depth data is generated by imitating the imaging principle of real-world sensors. RaSim can be directly applied to real-world scenarios without any finetuning and excel at downstream RGB-D perception tasks.
  arXiv  Detail & Related papers  (2024-04-05T08:52:32Z)
• Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution [81.74583887661794]
  We build a new real-world super-resolution benchmark with both integer and non-integer scaling factors. We propose a Dual-level Deformable Implicit Representation (DDIR) to solve real-world scale arbitrary super-resolution. Our trained model achieves state-of-the-art performance on the RealArbiSR and RealSR benchmarks for real-world scale arbitrary super-resolution.
  arXiv  Detail & Related papers  (2024-03-16T13:44:42Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Rethinking Blur Synthesis for Deep Real-World Image Deblurring [4.00114307523959]
  We propose a novel realistic blur synthesis pipeline to simulate the camera imaging process. We develop an effective deblurring model that captures non-local dependencies and local context in the feature domain simultaneously. A comprehensive experiment on three real-world datasets shows that the proposed deblurring model performs better than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-09-28T06:50:16Z)

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.