摘要
针对现有的深度获取方式存在数据缺失、分辨率低等问题,提出一种基于软聚类的深度图增强方法,称为软聚类求解器.该方法利用软聚类的强边缘保持特性提高深度图增强的精度.将软聚类仿射矩阵和加权最小二乘模型有机结合,构建了软聚类求解器中的置信加权最小二乘模型,提出了基于迭代的求解方法.为评估所提出的方法,在多项深度图增强任务上进行试验,包括深度图补洞、深度图超分辨率和深度图纠正,评价指标包含了峰值信噪比(PSNR)、结构相似度(SSIM)、均方根差(RMSE)和运行效率.结果表明:文中方法在深度图补洞任务中的平均PSNR达到了42.28,平均SSIM达到了98.83%;在深度图超分辨率、深度图纠正任务中的平均RMSE达到了8.96、 2.36.文中方法处理1张分辨率为2 048×1 024像素的图像仅需5.03 s.
To solve the problems of the existing depth acquisition methods with missing data and low resolution,the novel depth enhancement method based on soft clustering was proposed and named the soft clustering solver.By the strong edge-preservation of the soft clustering method,the accuracy of depth enhancement could be improved.The affinity matrix derived from the soft clustering was combined with the weighted least square model to establish the confidence-weighted least square model in the solver,and the iteratively based solution method was proposed.To evaluate the proposed method,the experiments on several depth enhancement tasks of depth inpainting,depth super-resolution and depth rectification were conducted.Various evaluation metrics were used,including peak signal to noise ratio(PSNR),structural similarity index measure(SSIM),rooted mean squared error(RMSE)and running time.The results show that for depth inpainting,the average PSNR reaches 42.28 with average SSIM of 98.83%.For depth super-resolution and depth rectification,the average RMSE values are 8.96 and 2.36,respectively.By the proposed method,the image with resolution of 2048×1024 pixels can be processed with only 5.03 s.
作者
杨洋
何童瑶
詹永照
赵岩
王新宇
YANG Yang;HE Tongyao;ZHAN Yongzhao;ZHAO Yan;WANG Xinyu(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China)
出处
《江苏大学学报（自然科学版）》
CAS
北大核心
2024年第2期183-190,共8页
Journal of Jiangsu University：Natural Science Edition
基金
国家自然科学基金资助项目(61902155,61402205)
江苏大学高级人才基金资助项目(19JDG024,13JDG085)。
关键词
图像处理
计算机视觉
加权最小二乘
深度图增强
置信度
软聚类
三维空间
image processing
computer vision
weighted least square
depth image enhancement
confidence
soft clustering
3D space
