Pyramid input-based network for single image dehazing

Yinxue Wei; Jinjiang Li; Zhen Hua

doi:10.1117/12.2675191

25 May 2023 Pyramid input-based network for single image dehazing

Yinxue Wei, Jinjiang Li, Zhen Hua

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263638 (2023) https://doi.org/10.1117/12.2675191
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Images taken in hazy weather are susceptible to the effects of haze, resulting in blurred images and low contrast to the extent that important information is lost in the image. Therefore, this is necessary to dehaze haze images, process the image information and ensure the normal operation of other computer vision tasks. Traditional deep learning-based image dehazing methods often suffer from uneven haze removal, colour bias and loss of detail. To solve this problem, this paper proposes a single image dehazing method (IMNet) based on pyramidal input for image dehazing. The network is divided into three modules: an intensive feature extraction module, a pyramid input branch and a detail deepening module. This paper uses two loss functions in combination, which can help preserve texture details more effectively. Experimental results have shown that IMNet outperforms other dehazing algorithms in terms of metrics and visual effects.

Citation Download Citation

Yinxue Wei, Jinjiang Li, and Zhen Hua "Pyramid input-based network for single image dehazing", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263638 (25 May 2023); https://doi.org/10.1117/12.2675191

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KEYWORDS

Feature extraction

Image processing

Air contamination

Convolution

Image enhancement

Distortion

Ablation

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