wavitedehaze - network:一种基于小波的低参数实时去雾方法

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ali Murtaza, Uswah Khairuddin, Ahmad ’Athif Mohd Faudzi, Kazuhiko Hamamoto, Yang Fang, Zaid Omar
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引用次数: 0

摘要

尽管近年来图像去雾问题受到了相当多的关注,但现有的模型往往以牺牲复杂性为代价优先考虑性能,这使得它们不适合现实世界的应用,这需要在资源受限的设备上部署算法。为了应对这一挑战,我们提出了wavitedehaze - network (WLD-Net),这是一种端到端去雾模型,在实时操作和使用更少参数的同时,提供与复杂模型相当的性能。这种方法利用了雾霾主要影响低频信息的洞察力。利用离散小波变换(DWT)在频域对图像进行单独处理,将图像分离为高、低频并分别进行处理。这使我们能够保留高频细节并恢复受雾霾影响的低频分量,将我们的方法与使用空间域处理作为主干,DWT作为辅助分量的现有方法区分开来。DWT应用于多个级别,以获得更好的信息保留,同时通过降采样特征映射加速计算。随后,基于学习的融合机制将处理后的频率重新整合以重建去噪图像。实验表明,WLD-Net在现实世界的雾霾图像上优于其他低参数模型,并与更大的模型竞争,在O-Haze数据集上获得了最高的PSNR和SSIM分数。定性地说,所提出的方法证明了它在处理各种雾霾类型方面的有效性,提供了视觉上令人愉悦的结果和强大的性能,同时也可以很好地推广到不同的场景。WLD-Net仅使用385万个参数(比同类除雾方法小100倍以上),在0.045秒内处理1024 × 1024的图像,突出了其在各种现实场景中的适用性。代码可在https://github.com/AliMurtaza29/WLD-Net上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

WaveLiteDehaze-Network: A Low-Parameter Wavelet-Based Method for Real-Time Dehazing

WaveLiteDehaze-Network: A Low-Parameter Wavelet-Based Method for Real-Time Dehazing

WaveLiteDehaze-Network: A Low-Parameter Wavelet-Based Method for Real-Time Dehazing

WaveLiteDehaze-Network: A Low-Parameter Wavelet-Based Method for Real-Time Dehazing

Although the image dehazing problem has received considerable attention over recent years, the existing models often prioritise performance at the expense of complexity, making them unsuitable for real-world applications, which require algorithms to be deployed on resource constrained-devices. To address this challenge, we propose WaveLiteDehaze-Network (WLD-Net), an end-to-end dehazing model that delivers performance comparable to complex models while operating in real time and using significantly fewer parameters. This approach capitalises on the insight that haze predominantly affects low-frequency information. By exclusively processing the image in the frequency domain using discrete wavelet transform (DWT), we segregate the image into high and low frequencies and process them separately. This allows us to preserve high-frequency details and recover low-frequency components affected by haze, distinguishing our method from existing approaches that use spatial domain processing as the backbone, with DWT serving as an auxiliary component. DWT is applied at multiple levels for better information retention while also accelerating computation by downsampling feature maps. Subsequently, a learning-based fusion mechanism reintegrates the processed frequencies to reconstruct the dehazed image. Experiments show that WLD-Net outperforms other low-parameter models on real-world hazy images and rivals much larger models, achieving the highest PSNR and SSIM scores on the O-Haze dataset. Qualitatively, the proposed method demonstrates its effectiveness in handling a diverse range of haze types, delivering visually pleasing results and robust performance, while also generalising well across different scenarios. With only 0.385 million parameters (more than 100 times smaller than comparable dehazing methods), WLD-Net processes 1024 × 1024 images in just 0.045 s, highlighting its applicability across various real-world scenarios. The code is available at https://github.com/AliMurtaza29/WLD-Net.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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