Aug 2026· International Conference on Digital Image Processing· Vol 14351, pp. 1435102 - 1435102-9· 0 citations· 15 references
Engineering
TL;DR
A dehazing framework named DKS-Net is proposed which fully utilizes the physics guiding features and extracting structural information in the spatial domain, and a Kernel Selective Feature Extraction Module (KSFE) is introduced to effectively captures structural patterns via large-kernel convolutions with dynamic selection capabilities and multi-scale semantic cues.
Abstract
The performance of deep learning-based dehazing frameworks inevitably degrades when applied to real-world scenarios, primarily due to the severe domain shift and diverse degradation types. A major bottleneck is that existing literature heavily relies on the raw pixel domain, thereby neglecting the distinct spectral characteristics of hazy observations and underutilizing the latent representation capacity of deep networks for high-quality image reconstruction. Concurrently, while attention-driven feature fusion has advanced image restoration, current formulations often suffer from inadequate dimension-wise feature interactions and coarse integration strategies. Furthermore, long-range contextual dependencies and global low-frequency illumination properties inherent in hazing processes remain largely unexploited. In this paper, we propose a dehazing framework named DKS-Net which fully utilizes the physics guiding features and extracting structural information in the spatial domain. Motivated by the realization that the learning of information at multiple scales and frequency bands are important for the deep networks, we introduce a Kernel Selective Feature Extraction Module(KSFE) to effectively captures structural patterns via large-kernel convolutions with dynamic selection capabilities and multi-scale semantic cues. With the above techniques, our method can show state-of-the-art(SOTA) performance on synthetic datasets and real-world datasets, achieving competitive performance in visual quality.
An efficient aligned kernel network (AKNet) is proposed, which innovatively employs super-large convolution kernels to capture global receptive fields with minimal computational overhead, effectively mimicking the long-range dependency modeling of transformers.
Wan Li, Xiao-Lin Zhang· The Visual Computer· 0 citations
Single-image dehazing remains a challenging low-level vision task because haze degradation is inherently depth-dependent and spatially non-uniform. To address this problem, we propose DMSH-Net, a Depth-Aware Multi-Scale Hybrid Vision Network specifically designed for robust single-image dehazing. DMSH-Net is designed to implicitly capture haze variations through hierarchical feature recalibration, nonlinear residual refinement, and multi-scale contextual aggregation. Specifically, we introduce a redesigned convolutional squeeze-and-excitation attention (CSEA) module, which replaces fully connected transformations with convolutional operations and global average pooling to jointly model channel dependencies and spatial context. Building on CSEA, a nonlinear CSEA-coupled residual block (NCCRB) is developed to enhance local feature representation and improve adaptability to haze with varying densities. Furthermore, a multi-scale dilated convolution bottleneck is incorporated to enlarge the receptive field and aggregate haze-aware contextual information across multiple spatial scales, thereby improving the restoration of regions with varying scene depths. Extensive experiments on standard benchmarks demonstrate that DMSH-Net consistently achieves superior quantitative performance across full-reference and no-reference evaluations, thereby validating its robustness in complex real-world dehazing scenarios.
Chenping Zhao, Jun Li, Yingjun Wang et al.· PLoS ONE· 0 citations
Real-world remote sensing image dehazing (RSID) remains challenging because atmospheric scattering, spatially non-uniform haze and colour distortion jointly degrade structural and spectral information. Most deep learning methods rely on RGB inputs and spatial-domain feature extraction, which limits their ability to separate global background haze from local surface details. Here, we propose DPSF-Net, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID. The network uses hazy RGB images and dark channel prior (DCP) maps as joint inputs, allowing physical degradation cues to guide end-to-end feature learning. A spatial-frequency residual interaction block introduces a FourierUnit branch into multi-directional spatial interaction to model large-scale haze components. A prior-guided feature attention module adaptively fuses prior and attention features to reduce colour shift and structural distortion. A selective kernel complementary fusion module screens multi-scale skip features through bidirectional residual complementary gating and selective kernel fusion. Extensive experiments demonstrate that DPSF-Net achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets. Moreover, the proposed method strikes a favourable balance among restoration quality, parameter count and computational complexity, supporting the effectiveness of dual-prior spatial-frequency modelling.
Mei Lu, Shang-Liang Shao, Shan-Liang Yao· 0 citations
Image dehazing aims to generate the haze-free images from the hazy observation images. While recent deep learning approaches achieve impressive restoration quality, they suffer from excessive computational complexity and model size, hindering practical applications for real-world deployment on resource-constrained edge devices. To address the limitation, lightweight models are proposed to this end but compromise on dehazing performance. To bridge this gap, we propose SLRNet, Super Lightweight Residual Network, a high efficient-yet-effective end-to-end dehazing architecture. SLRNet integrates a novel Adaptive Feature Unit that automatically adjusts channel-wise features through a lightweight gating mechanism, coupled with compact residual blocks to preserve critical structural information. Unlike standard channel attention mechanisms that discard spatial information, our AFU employs an asymmetric split strategy to simultaneously preserve local texture details and capture global haze density. Our design emphasizes minimal parameter count and low latency without sacrificing perceptual quality. Experiments are carried out across standard benchmarks, showing that our proposed SLRNet demonstrates remarkable performance by achieving state-of-the-art efficiency-accuracy trade-offs compared to existing works, while maintaining robust generalization to real-world haze despite the synthetic-to-real domain gap. The codes are released in https://anonymous.4open.science/r/SLRNet.
Guanheng Qu· Poster Volume 0007 The 2026...· 0 citations
Due to the limited dynamic range of imaging sensors, most cameras can only capture low-dynamic-range (LDR) images. Multi-exposure fusion (MEF) is an effective technique for generating high-dynamic-range (HDR) images. However, to simultaneously preserve texture details and global exposure, most existing methods primarily rely on more complex models to improve performance, resulting in higher computational costs and longer processing times. To address this issue, we propose a real-time unsupervised MEF network driven by prior knowledge. To this end, a hierarchical feature extraction module is designed that utilizes filtering operations to decompose the source images into base layers and detail layers. Features are extracted from each layer separately to reduce the difficulty of extracting effective features. Then, the receptive field of feature maps is expanded by dilated convolutions, and a window-based self-attention mechanism is applied to perform context modeling, achieving effective contextual modeling with low computational cost. Subsequently, texture features and global features are extracted separately to enable the model to maintain both local texture clarity and global smoothness. In addition, a one-dimensional lookup table is utilized to accelerate the inference process. Comprehensive experiments are conducted to verify the effectiveness of the proposed method. The subjective evaluation results demonstrate that the fused images exhibit superior visual quality, while objective experiments further quantify its superior performance, demonstrating that the proposed method effectively reduces computation time.
Junwei Qi, Hang-Dong Wang, Xu Xiao et al.· Journal of Imaging· 0 citations
Remote sensing imagery is highly susceptible to haze, which can obscure visibility and limit the reliability of downstream analysis tasks, making aerial image dehazing critical for space and defense applications. Existing methods often fail to faithfully restore structural details and color fidelity under spatially varying dense haze and are parameter-intensive, limiting their deployment in resource-constrained environments. To address this issue, we propose a lightweight encoder–decoder framework for aerial image dehazing that jointly models anisotropic spatial characteristics and global contextual dependencies. The proposed multiscale directional feature fusion (MDFF) module explicitly encodes directional and enlarged receptive-field interactions to capture spatially varying haze distribution, while the haze region-aware refinement (HRAR) module estimates haze regions and refines degraded features through dual-stage attention for guided feature learning. In addition, the Hadamard-gated feature modulation (HGFM) module introduces parameter-efficient multiplicative feature recalibration to enhance fine textural details. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior restoration performance while maintaining substantially fewer parameters and higher computational efficiency. The code will be publicly available at: https://github.com/Shiladityagit/Lightweight_Aerial_Dehazing.
Shiladitya Mondal, S. K. Dhara, Anusha Vupputuri· IEEE Geoscience and Remote S...· 0 citations
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