SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism

Sun, Ziyin; Zhang, Yunfeng; Bao, Fangxun; Wang, Ping; Yao, Xunxiang; Zhang, Caiming

doi:10.1145/3478457

Cited by 15 publications

(7 citation statements)

References 55 publications

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“…Furthermore, an effective network was introduced in [ 29 ] it comprises a residual spatial and channel attention module to adaptively adjust feature weights, considering haze distribution, enhancing feature representation and dehazing performance. Moreover, Sun et al [ 30 ] have proposed a fast and robust semi-supervised dehazing method (SADnet) that incorporates both channel and spatial attention mechanisms. This technique shows its effectiveness in haze removal; however, it produces some color artefacts on dehazing outcomes.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)

Haouassi,

2024

Sensors

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Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing model (ADMC2-net)that successfully incorporates both RGB and HSV color spaces to maintain color properties. This model consists of two parallel densely connected sub-models (RGB and HSV) followed by a new efficient attention module. This attention module comprises pixel-attention and channel-attention mechanisms to get more haze-relevant features. Experimental results analyses can validate that our proposed model (ADMC2-net) can achieve superior results on synthetic and real-world datasets and outperform most of state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

“…It is evident that the above-discussed attention-based models [ 26 , 27 , 28 , 29 , 30 ] can exhibit notable improvements in enhancing dehazing robustness. They outperform existing end-to-end models, showcasing their efficacy in addressing haze-related challenges.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)

Haouassi,

2024

Sensors

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“…Ref. [ 39 ] proposes a semi-supervised learning network: SAD-Net. SAD-Net utilizes both synthetic datasets and natural hazy images for training and uses an attention mechanism to increase dehazing performance.…”

Section: Related Workmentioning

confidence: 99%

Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing

Wang,

Jia,

Lyu

et al. 2023

J. Imaging

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Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, many dehazing networks aim to enhance performance by adopting intricate network architectures, complicating training, inference, and deployment procedures. This study proposes an end-to-end U-Net dehazing network model with recursive gated convolution and attention mechanisms to improve performance while maintaining a lean network structure. In our approach, we leverage an improved recursive gated convolution mechanism to substitute the original U-Net’s convolution blocks with residual blocks and apply the SK fusion module to revamp the skip connection method. We designate this novel U-Net variant as the Dehaze Recursive Gated U-Net (DRGNet). Comprehensive testing across public datasets demonstrates the DRGNet’s superior performance in dehazing quality, detail retrieval, and objective evaluation metrics. Ablation studies further confirm the effectiveness of the key design elements.

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“…Compared with general object detection, few research efforts have been explored on object detection in adverse weather conditions. Early methods mainly focused on pre‐processing the degraded images by existing restoration algorithms such as image dehazing [HST11, QWB*20, WYG*22, SZB*22] or image deraining [LQS*19, RLHS20a, DWW*20], and then sending the processed images to the subsequent detection network for object detection. Although employing image restoration approaches as a preprocessing step can improve the overall quality of degraded images, these images may not definitely benefit the detection performance.…”

Section: Related Workmentioning

confidence: 99%

TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

Wang

Yan

Zhang

et al. 2022

Computer Graphics Forum

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Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question -if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre-processing step, TogetherNet considers a multi-task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real-world datasets demonstrate that our TogetherNet outperforms the state-of-the-art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yz-wang/TogetherNet.

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SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention Mechanism

Cited by 15 publications

References 55 publications

An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)

An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)

Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing

TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

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