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基于深度学习的船舶舱外监控图像去雨研究

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由于降雨条纹会严重降低监控图像的识别效果,从而延误船舶驾驶员对航行态势的感知与判断,现有方法不能适应不同密度大小的雨条纹,并且无法有效保留背景细节。为解决这一问题,提出了一种基于深度学习的图像去雨网络,该网络可以呈现雨天图像和干净图像之间的细节层中的残差,细节层能够将信息从下层传播到上层,而上层由级联网络组成。实验结果和数据分析表明,提出的方法在人工合成图像和自然真实图像上都能表现出良好的去雨效果,主观评价和客观评价均优于其他对比算法。
Research on Rain Removal from Ship Outside Cabin Monitoring Images Based on Deep Learning
Due to the significant decrease in the recognition performance of monitoring images caused by rain streaks, it delays the perception and judgment of navigation situations by ship drivers. Existing methods cannot adapt to rain streaks of different densities and sizes, and cannot effectively preserve background details. To solve this issue, a deep learning based image rain removal network is proposed, which can present residuals in the detail layer between rainy and clean images. The detail layer can propagate information from the lower layer to the upper layer, which is composed of a cascaded network. The experimental results and data analysis show that the proposed method can perform well in removing rain on both artificially synthesized images and natural real images, with subjective and objective evaluations superior to other comparative algorithms.

image rain removaldeep learningcascaded networkmulti-scale residualattention mechanism

王政

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陆军军事交通学院镇江校区,江苏镇江212003

图像去雨 深度学习 级联网络 多尺度残差 注意力机制

2024

船舶职业教育

船舶职业教育

ISSN:
年,卷(期):2024.12(3)
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