Contrast Aware Image Dehazing using Generative Adversarial Network

Parihar, Anil Singh; Singh, Khushboo; Ganotra, Aryan; Yadav, Aviral; Devashish,

doi:10.1109/conit55038.2022.9847710

Cited by 7 publications

(1 citation statement)

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“…This model directly generates clean images from blurred images, rather than relying on any separate intermediate parameter estimation steps. Recently, many methods have used end-to-end learning instead of atmospheric scattering models to directly obtain clean images from networks [22][23][24][25]. Another widely used method tends to predict the residual of potential haze-free images or haze-free images relative to hazy images, as they often achieve better performance [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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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