计算机工程 ›› 2019, Vol. 45 ›› Issue (4): 228-234.doi: 10.19678/j.issn.1000-3428.0050656

• 图形图像处理 • 上一篇    下一篇

基于递阶辨识与交替方向乘子法的深度图像增强

张跃,朱启兵,黄敏,李浩   

  1. 江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
  • 收稿日期:2018-03-07 出版日期:2019-04-15 发布日期:2019-04-15
  • 作者简介:张跃(1992—),女,硕士研究生,主研方向为深度图像处理;朱启兵(通信作者)、黄敏,教授;李浩,硕士研究生。
  • 基金项目:

    国家自然科学基金(61772240);江苏省政策引导类计划(产学研合作)-前瞻性联合研究项目(BY2016022-32);江苏省研究生科研与实践创新计划项目(SJCX17_0508)。

Depth Image Enhancement Based on Hierarchical Identification and Alternating Direction Multiplier Method

ZHANG Yue,ZHU Qibing,HUANG Min,LI Hao   

  1. Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education, Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:2018-03-07 Online:2019-04-15 Published:2019-04-15

摘要:

针对主流传感器采集的深度图像存在深度信息区域缺失、噪声等图像质量问题,提出一种基于SD全局优化模型的深度图像增强算法。采用非凸函数对SD全局优化模型平滑项进行建模,使其对异常值具有较强的鲁棒性。使用基于递阶辨识(HI)的交替方向乘子法求解SD全局优化模型,将目标函数分解成多个子目标函数,并对每个子目标函数通过HI思想进行逐个求解,降低求解复杂度。实验结果表明,该算法在加快收敛速度的同时,能有效去除图像噪声及抑制深度伪影。

关键词: 彩色引导, 深度图像增强, 全局优化, 非凸函数, 递阶辨识, 交替方向乘子法

Abstract:

Because the depth image acquired by the mainstream sensor has image quality problems such as missing depth information area and noise,a depth image enhancement algorithm based on Static/Dynamic(SD) global optimization model is proposed.The SD global optimization model smoothing term is modeled by non-convex functions,which makes it more robust to outliers.The Alternating Direction Multiplier Method(ADMM) based on Hierarchical Identification(HI) is used to solve the SD global optimization model.The method decomposes the objective function into multiple sub-objective functions,and solves each sub-objective function one by one through the HI idea to reduce the complexity of the solution.Experimental results show that the proposed algorithm can effectively remove image noise and suppress depth artifacts while speeding up convergence.

Key words: color guided, depth image enhancement, global optimization, non-convex function, Hierarchical Identification(HI), Alternating Direction Multiplier Method(ADMM)

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