Physical-priors-guided DehazeFormer

Zhou, Hao; Chen, Zekai; Liu, Yun; Sheng, Yongpan; Ren, Wenqi; Xiong, Hao

doi:10.1016/j.knosys.2023.110410

Cited by 12 publications

(2 citation statements)

References 63 publications

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“…This category of algorithms is predicated on the principles of atmospheric scattering theory, resolving the atmospheric scattering (Eq. ( 1)) by imbuing it with supplementary conditions to render it nonpathological [24,25]. These algorithms leverage diverse a priori information as auxiliary inputs to estimate the parameters λ, η, and β in Eq.…”

Section: Physical Model-based Defogging Algorithmmentioning

confidence: 99%

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Liu,

Deng,

Shao

2024

CMES

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The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach materializes as an endto-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump connections. The essence of our methodology lies in local feature fusion within dense residual clusters, enabling the extraction of pertinent features from both preceding and current local data, depending on contextual demands. The intelligently orchestrated gated structures facilitate the propagation of these features to the decoder, resulting in superior outcomes in haze removal. Empirical validation through a plethora of experiments substantiates the efficacy of URA-Net, demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image defogging. On the RICE-1 dataset, URA-Net achieves a Peak Signal-to-Noise Ratio (PSNR) of 29.07 dB, surpassing the Dark Channel Prior (DCP) by 11.17 dB, the All-in-One Network for Dehazing (AOD) by 7.82 dB, the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing (OTM-AAL) by 5.37 dB, the Unsupervised Single Image Dehazing (USID) by 8.0 dB, and the Superpixelbased Remote Sensing Image Dehazing (SRD) by 8.5 dB. Particularly noteworthy, on the SateHaze1k dataset, URA-Net attains preeminence in overall performance, yielding defogged images characterized by consistent visual quality. This underscores the contribution of the research to the advancement of remote sensing technology, providing a robust and efficient solution for alleviating the adverse effects of haze on image quality.

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Section: Physical Model-based Defogging Algorithmmentioning

confidence: 99%

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Liu,

Deng,

Shao

2024

CMES

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“…Li et al [11] improved ASM and proposed AODNet. In recent years, the relatively novel dehazing work has included end-to-end methods [12][13][14][15], attention mechanism methods [16,17], weakly supervised methods [18], semisupervised methods [19][20][21], knowledge-distillation-based methods [22,23], contrastivelearning-based methods [24,25], and Transformer-based methods [26][27][28][29]. Although these methods have been very successful, these dehazing network models are often accompanied by complex network structures and large model parameters, which have high computational overhead.…”

Section: Introductionmentioning

confidence: 99%

Dehaze-UNet: A Lightweight Network Based on UNet for Single-Image Dehazing

Zhou,

Chen,

et al. 2024

Electronics

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Numerous extant image dehazing methods based on learning improve performance by increasing the depth or width, the size of the convolution kernel, or using the Transformer structure. However, this will inevitably introduce many parameters and increase the computational overhead. Therefore, we propose a lightweight dehazing framework: Dehaze-UNet, which has excellent dehazing performance and very low computational overhead to be suitable for terminal deployment. To allow Dehaze-UNet to aggregate the features of haze, we design a LAYER module. This module mainly aggregates the haze features of different hazy images through the batch normalization layer, so that Dehaze-UNet can pay more attention to haze. Furthermore, we revisit the use of the physical model in the network. We design an ASMFUN module to operate the feature map of the network, allowing the network to better understand the generation and removal of haze and learn prior knowledge to improve the network’s generalization to real hazy scenes. Extensive experimental results indicate that the lightweight Dehaze-UNet outperforms state-of-the-art methods, especially for hazy images of real scenes.

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Exploring the potential of channel interactions for image restoration

Cui,

Knoll

2023

Knowledge-Based Systems

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Physical-priors-guided DehazeFormer

Cited by 12 publications

References 63 publications

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Dehaze-UNet: A Lightweight Network Based on UNet for Single-Image Dehazing

Exploring the potential of channel interactions for image restoration

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