Encoder and Decoder-Based Feature Fusion Network for Single Image Dehazing

Shit, Sahadeb; Das, Dibyendu; Sur, Amit; Ray, Dip Narayan; Banik, Bipasha Chakrabarti; Rana, Aiswarya

doi:10.1109/aisp57993.2023.10135067

Cited by 2 publications

(2 citation statements)

References 29 publications

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Section: Haze Removal Methodsmentioning

confidence: 99%

“…Different approaches, such as spatial and channel attention, can be employed to integrate attention mechanisms into dehazing models to minimize the feature loss between encoder and decoder modules. [81][82][83] An illustration of attention mechanism-based dehazing methods includes the AOD-Net 67 and AdaFM-Net. 12 The AOD-Net 67 adopts a multi-scale CNN architecture 64 that incorporates a spatial attention module.…”

Section: Attention Mechanisms Incorporating Attention Mechanisms Into...mentioning

confidence: 99%

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Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

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Section: Haze Removal Methodsmentioning

confidence: 99%

Section: Attention Mechanisms Incorporating Attention Mechanisms Into...mentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing

Leng,

Liu

2023

Applied Sciences

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In recent years, numerous single-image dehazing algorithms have made significant progress; however, dehazing still presents a challenge, particularly in complex real-world scenarios. In fact, single-image dehazing is an inherently ill-posed problem, as scene transmission relies on unknown and nonhomogeneous depth information. This study proposes a novel end-to-end single-image dehazing method called the Integrated Feature Extraction Network (IFE-Net). Instead of estimating the transmission matrix and atmospheric light separately, IFE-Net directly generates the clean image using a lightweight CNN. During the dehazing process, texture details are often lost. To address this issue, an attention mechanism module is introduced in IFE-Net to handle different information impartially. Additionally, a new nonlinear activation function is proposed in IFE-Net, known as a bilateral constrained rectifier linear unit (BCReLU). Extensive experiments were conducted to evaluate the performance of IFE-Net. The results demonstrate that IFE-Net outperforms other single-image haze removal algorithms in terms of both PSNR and SSIM. In the SOTS dataset, IFE-Net achieves a PSNR value of 24.63 and an SSIM value of 0.905. In the ITS dataset, the PSNR value is 25.62, and the SSIM value reaches 0.925. The quantitative results of the synthesized images are either superior to or comparable with those obtained via other advanced algorithms. Moreover, IFE-Net also exhibits significant subjective visual quality advantages.

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Encoder and Decoder-Based Feature Fusion Network for Single Image Dehazing

Cited by 2 publications

References 29 publications

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing

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