深度上采样的自适应双边滤波去噪

Donghoon Yeo, Ehsan ul haq, Jongdae Kim, Mirza Waqar Baig, Hyunchu Shin
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引用次数: 21

摘要

3D场景渲染需要深度图和颜色信息来产生高质量的3D结果。遗憾的是,使用飞行时间(TOF)相机捕获的深度图分辨率有限,图像质量差,受到随机和系统噪声的严重影响,这使得它们不适合生成高质量的3D图像。本文进一步分析了一种深度图分辨率上采样的框架,该框架将低分辨率深度图像素的空间和深度差的高斯分布与同一场景的高分辨率二维彩色图像的颜色强度差的高斯分布结合起来。高斯函数的方差控制着单规划区域的平滑量和边界的清晰度。使用更大的方差平滑单规划区域,但模糊边缘,反之亦然。提出了一种自适应计算和利用方差的方法,使上采样深度图表面更光滑,边缘更锐利,噪声最小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive bilateral filtering for noise removal in depth upsampling
3D scene rendering requires depth maps and color information to produce high quality 3D results. Unfortunately, depth maps captured with the Time-of-flight (TOF) cameras have limited resolution and poor image quality, being severely influenced by the random and systematic noise, which makes them inapposite for generating high quality 3D images. In this paper, we have further analyzed a framework for upsampling the resolution of depth maps that jointly uses Gaussians of spatial and depth differences of low resolution depth map's pixels along with Gaussian of color intensity difference from high resolution 2D color image of the same scene. The variance of the Gaussian functions controls the amount of smoothing in uni-planner area and sharpness at boundaries. Using bigger variance smooths uni-planner area but blurs edges and vice versa. We have devised a method to adaptively calculate and use variance to get smoother surface and sharper edges of upsampled depth map with minimized noise.
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