Ring Difference Filter for Fast and Noise Robust Depth From Focus

IF 10.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hae-Gon Jeon, Jaeheung Surh, Sunghoon Im, I. Kweon
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引用次数: 20

Abstract

Depth from focus (DfF) is a method of estimating the depth of a scene by using information acquired through changes in the focus of a camera. Within the DfF framework of, the focus measure (FM) forms the foundation which determines the accuracy of the output. With the results from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable. In this paper, we propose a new FM, which we call the “ring difference filter” (RDF), that can more accurately and robustly measure focus. FMs can usually be categorized as confident local methods or noise robust non-local methods. The RDF’s unique ring-and-disk structure allows it to have the advantages of both local and non-local FMs. We then describe an efficient pipeline that utilizes the RDF’s properties. Part of this pipeline is our proposed RDF-based cost aggregation method, which is able to robustly refine the initial results in the presence of image noise. Our method is able to reproduce results that are on par with or even better than those of state-of-the-art methods, while spending less time in computation.
环形差值滤波器的快速和噪声鲁棒深度从焦点
焦点深度(Depth from focus, DfF)是一种利用相机焦点变化获取的信息来估计景深的方法。在DfF框架内,焦点测量(FM)是决定输出精度的基础。根据FM的结果,DfF管道的作用是确定和重新计算不可靠的测量,同时增强那些可靠的测量。本文提出了一种新的调频方法,我们称之为“环差滤波器”(RDF),它可以更准确和鲁棒地测量焦点。FMs通常可以分为置信局部方法和噪声鲁棒非局部方法。RDF独特的环盘结构允许它同时具有本地和非本地fm的优点。然后,我们描述了一个利用RDF属性的高效管道。该管道的一部分是我们提出的基于rdf的成本聚合方法,该方法能够在存在图像噪声的情况下稳健地改进初始结果。我们的方法能够再现与最先进的方法相当甚至更好的结果,同时花费更少的计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing 工程技术-工程:电子与电气
CiteScore
20.90
自引率
6.60%
发文量
774
审稿时长
7.6 months
期刊介绍: The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.
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