融合场景深度估计和视觉传达的复杂光照图像虚拟重建
Virtual reconstruction of complex illumination image based on scene depth estimation and visual communication
柴萍 1柴金娣2
作者信息
- 1. 甘肃民族师范学院,甘肃合作 747000
- 2. 甘肃省非物质文化遗产中心,兰州 730000
- 折叠
摘要
复杂光照图像虚拟中受到光照强度不均衡性影响导致重建效果不好,为了提高复杂光照图像虚拟重建效果,提出基于融合场景深度估计和视觉传达的复杂光照图像虚拟重建方法.针对不同场景深度混频光照的相互干扰采用相关匹配降噪方法实现图像降噪处理,以光照图像低亮度区域内亮度值中位数作为场景深度的参考值,采用全局特性和局部细节特征拟合的方法实现对复杂光照图像的场景深度检测和视觉跟踪拟合,采用HSV空间特征分解方法实现对不同场景中光照图片亮度通道融合处理,提取场景物体边缘、纹理等细节信息,根据场景深度检测和全局对比度融合下的视觉传达效果实现复杂光照图像虚拟重建.测试结果得知,采用该方法进行复杂光照图像虚拟重建的视觉表达能力较好,重建后的图像细节展示能力较强,能准确重建暗区域内隐藏的图像信息,两个数据集图像的峰值信噪比较高,均方根误差较低,分别为45.63 dB、53.21 dB和0.366、0.265,且重建时长短,仅为1.5 s,具有较强的重建性能.
Abstract
In order to improve the virtual reconstruction effect of complex lighting images,a virtual reconstruction method for complex lighting images based on fusion of scene depth estimation and visual communication is proposed.Aiming at the mutual interference of mixed frequency illumination at different scene depths,a correlation matching noise reduction method is used to achieve image noise reduction processing.The median brightness value in the low brightness region of the illumination image is used as the reference value for scene depth,and the method of global characteristics and local detail feature fitting is used to achieve scene depth detection and visual tracking fitting for complex illumination images,The HSV spatial feature decomposition method is used to fuse the brightness channels of lighting images in different scenes,extract detailed information such as scene object edges and textures,and achieve virtual reconstruction of complex lighting images based on visual communication effects under scene depth detection and global contrast fusion.The simulation test results show that using this method to perform virtual reconstruction of com-plex lighting images has a good visual expression ability,and the reconstructed image has a strong ability to display de-tails.It can accurately reconstruct hidden image information in dark areas.The peak signal to noise ratio of the two dataset images is high,and the root mean square error is low,respectively 45.63 dB,53.21 dB,and 0.366,0.265.Moreover,the reconstruction time is short,and the maximum length is only 1.5 s,with strong reconstruction perform-ance.
关键词
融合场景深度/视觉传达/复杂光照图像/虚拟重建/细节特征Key words
fusion scene depth/visual communica-tion/complex lighting image/virtual reconstruction/de-tail characteristics引用本文复制引用
基金项目
甘肃省高校教师创新基金(2023A-157)
出版年
2024
NETL
NSTL
万方数据