Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

Xin WU; Xiaoyan LIU; Fei YUAN

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Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

机译：颗粒化过程图像图像去吸收算法的实验分析

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The product quality of pelletization process in steel industry is usually monitored by machine vision system. However, the image quality deteriorates significantly by haze generated during pelletization. Current image dehazing algorithms mainly concentrate on natural haze in outdoor or synthetic hazy images. Whether these algorithms can be directly adopted in solving haze removal problem in industrial process images, needs to be studied. In the present work, experiments are performed to compare the performance of five state-of-the-art image dehazing algorithms, using the image dataset PELLET that consists of real hazy images captured from pelletization process in a local steel company. For a comprehensive comparative study of the image dehazing algorithms, both qualitative and quantitative evaluation criteria are adopted, including visual perceptual evaluation, no-reference image quality assessment, and task-driven comparison. Our experimental analysis demonstrates that Boundary Constrained Context Regularization (BCCR) and Non-local (NLD) image dehazing algorithms generally achieve better quality of restored image than the other three algorithms (Dark-Channel Prior, Optimized Contrast Enhancement, and AOD deep learning network) in dealing with pelletization process images with different haze levels. The computing time needed by BCCR algorithm is only half of that by NLD (2.5 vs. 5 seconds) in processing a hazy image of size 656 × 490. Nevertheless, the performance of these algorithms needs to be improved in the future to deal with pelletization process images with dense haze, as well as to meet with the realtime requirement of pelletization process monitoring.

机译：钢铁工业造粒过程的产品质量通常通过机器视觉系统监测。然而，通过在造粒期间产生的雾度显着恶化。目前的图像脱水算法主要集中在室外或合成朦胧图像中的天然阴霾。是否可以直接采用这些算法在求解工业过程中的雾霾去除问题时，需要进行研究。在本工作中，进行实验以比较五种最先进的图像脱水算法的性能，使用由当地钢铁公司中的造粒过程中捕获的真实朦胧图像组成的图像数据集颗粒。对于图像脱水算法的全面比较研究，采用了定性和定量评估标准，包括视觉感知评估，无参考图像质量评估和任务驱动的比较。我们的实验分析表明，边界受限的上下文正则化（BCCR）和非本地（NLD）图像脱水算法通常达到比其他三种算法更好的恢复图像质量（暗信道先前，优化的对比度增强，以及AOD深度学习网络）在处理具有不同雾度水平的造粒过程图像时。 BCCR算法所需的计算时间仅在处理大小656×490的朦胧图像时仅由NLD（2.5与5秒）的一半。然而，在将来需要改进这些算法的性能以处理造粒处理具有密集雾度的图像，以及满足造粒过程监测的实时要求。

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来源
《ISIJ international》 |2021年第1期|269-279|共11页
作者
Xin WU; Xiaoyan LIU; Fei YUAN;
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作者单位

College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082 China;

College of Electrical and Information Engineering Hunan University Changsha 410082 China Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Changsha 410082 China;

College of Electrical and Information Engineering Hunan University Changsha 410082 China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
原文格式 PDF
正文语种 eng
中图分类
关键词
image dehazing; palletization; size distribution; machine vision;

机译：图像脱皮;托盘化;尺寸分布;机器视觉;

Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

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