For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method...For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details（edges）, the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.展开更多
焊接过程可视化监控与成形缺陷智能识别是实现焊接智能制造的重要途径之一.采用红外CCD在线采样熔化极气体保护焊(gas metal arc welding,GMAW)熔池红外图像,结合改进滤波算法和图像增强算法对图像进行预处理,通过热电偶进行温度标定,...焊接过程可视化监控与成形缺陷智能识别是实现焊接智能制造的重要途径之一.采用红外CCD在线采样熔化极气体保护焊(gas metal arc welding,GMAW)熔池红外图像,结合改进滤波算法和图像增强算法对图像进行预处理,通过热电偶进行温度标定,建立红外图像中灰度值与温度值的对应关系,进而获取焊接熔池的温度分布信息,然后采用改进边缘提取算法提取熔池的特征参数,据此建立焊接外观缺陷的特征识别算法.结果表明,所设计的算法对焊接形状缺陷、烧穿及未熔透等在线识别具有良好的实用性和准确性.展开更多
基金supported by the National Natural Science Foundation of China(No.61231014)the Foundation of Army Armaments Department of China(No.6140414050327)the Foundation of Science and Technology on Low-Light-Level Night Vision Laboratory(No.BJ2017001)
文摘For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement（FNCE） method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details（edges）, the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.
文摘焊接过程可视化监控与成形缺陷智能识别是实现焊接智能制造的重要途径之一.采用红外CCD在线采样熔化极气体保护焊(gas metal arc welding,GMAW)熔池红外图像,结合改进滤波算法和图像增强算法对图像进行预处理,通过热电偶进行温度标定,建立红外图像中灰度值与温度值的对应关系,进而获取焊接熔池的温度分布信息,然后采用改进边缘提取算法提取熔池的特征参数,据此建立焊接外观缺陷的特征识别算法.结果表明,所设计的算法对焊接形状缺陷、烧穿及未熔透等在线识别具有良好的实用性和准确性.
