Single image dehazing based on the fusion of multi-branch and attention mechanism

Yu, Xingwen; Yu, Huikang

doi:10.1109/bdicn55575.2022.00130

Cited by 1 publication

(5 citation statements)

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“…Incorporating multi-branch designs in network architectures aims to process distinct inputs through separate branches or capture various facets of the same input across multiple hierarchical levels. Below, we will delve into the details of the FMBAM [58], DPRN [59], and MSTN [60] networks, all of which employ this strategy to enhance their performance and capabilities.…”

Section: Multi-branch Designsmentioning

confidence: 99%

“…FMBAM:The Fusion of Multi-Branch and Attention Mechanisms (FMBAM) is a single-image dehazing network proposed by Yu et al [58]. This end-to-end dehazing network integrates attention-based feature fusion with transfer learning within a multi-branch network architecture.…”

Section: Multi-branch Designsmentioning

confidence: 99%

“…DPRN:The dual-path recurrent network (DPRN), a singleimage dehazing method, was introduced by Zhang et al [59]. In contrast to FMBAM [58], DPRN [59] tackles the dehazing challenge by simultaneously restoring the primary content and image features using a dual-path recurrence The dual-path block is a crucial element of DPRN, featuring two parallel branches dedicated to capturing the hazy image's fundamental content and intricate details. Each branch comprises convolutional LSTM blocks and convolutional layers.…”

Section: Multi-branch Designsmentioning

confidence: 99%

“…MSTN:Yi et al recently introduced an innovative dehazing framework named the Efficient and Accurate Multi-scale Topological Network for Single-Image Dehazing (MSTN) [60]. Unlike the strategies employed by FMBAM [58] and DPRN [59], MSTN [60] addresses the challenge of image dehazing through the utilization of a specially designed Multiscale Feature Fusion Module (MFFM) in conjunction with an Adaptive Feature Selection Module (AFSM). This strategic amalgamation enables the selective and effective integration of features from various scales.…”

Section: Multi-branch Designsmentioning

confidence: 99%

“…This strategic amalgamation enables the selective and effective integration of features from various scales. Consequently, MSTN excels at restoring clear information from hazy images, showcasing its distinct approach compared to prior methods such as FMBAM [58] and DPRN [59].…”

Section: Multi-branch Designsmentioning

confidence: 99%

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Delving Deeper Into Image Dehazing: A Survey

Li,

Chen

et al. 2023

IEEE Access

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Images captured under foggy or hazy weather conditions are affected by the scattering of atmospheric particles, resulting in decreased contrast and color variation, thereby limiting their practical applications. In recent years, deep learning methods showcase significant advancements in image dehazing. However, the complexity and degradation factors in hazy images challenge the generalization capacity of dehazing methods. This paper comprehensively reviews the recent developments in single-image dehazing techniques based on deep learning. From the perspectives of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), different models are introduced and classified into four categories: Encoder-Decoder, Multi-Module, Multi-Branch, and Dual-Generative Adversarial Networks. The robustness and effectiveness of deep learning models are analyzed by comparing their performance and model complexity on public datasets. Additionally, limitations of current benchmark datasets and evaluation metrics are identified, and unresolved issues and future research directions are discussed. Our efforts in this paper will serve as a comprehensive reference for future research and call for further development in deep learning-based image dehazing. INDEX TERMS Image dehazing, Deep learning, Convolutional neural networks (CNNs), Generative adversarial networks (GANs).

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Section: Multi-branch Designsmentioning

confidence: 99%

Section: Multi-branch Designsmentioning

confidence: 99%