High precision light field image depth estimation via multi-region attention enhanced network

IF 1.5 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IET Computer Vision Pub Date : 2024-12-10 DOI:10.1049/cvi2.12326
Jie Li, Wenxuan Yang, Chuanlun Zhang, Heng Li, Xinjia Li, Lin Wang, Yanling Wang, Xiaoyan Wang
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Abstract

Light field (LF) depth estimation is a key task with numerous practical applications. However, achieving high-precision depth estimation in challenging scenarios, such as occlusions and detailed regions (e.g. fine structures and edges), remains a significant challenge. To address this problem, the authors propose a LF depth estimation network based on multi-region selection and guided optimisation. Firstly, we construct a multi-region disparity selection module based on angular patch, which selects specific regions for generating angular patch, achieving representative sub-angular patch by balancing different regions. Secondly, different from traditional guided deformable convolution, the guided optimisation leverages colour prior information to learn the aggregation of sampling points, which enhances the deformable convolution ability by learning deformation parameters and fitting irregular windows. Finally, to achieve high-precision LF depth estimation, the authors have developed a network architecture based on the proposed multi-region disparity selection and guided optimisation module. Experiments demonstrate the effectiveness of network on the HCInew dataset, especially in handling occlusions and detailed regions.

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光场(LF)深度估算是一项具有众多实际应用的关键任务。然而,在遮挡和细节区域(如精细结构和边缘)等具有挑战性的场景中实现高精度深度估计仍然是一项重大挑战。为了解决这个问题,作者提出了一种基于多区域选择和引导优化的 LF 深度估计网络。首先,我们构建了基于角补丁的多区域差异选择模块,该模块选择特定区域生成角补丁,通过平衡不同区域实现具有代表性的子角补丁。其次,与传统的引导式可变形卷积不同,引导式优化利用颜色先验信息来学习采样点的聚集,通过学习变形参数和拟合不规则窗口来增强可变形卷积的能力。最后,为了实现高精度的 LF 深度估计,作者基于所提出的多区域差异选择和引导优化模块开发了一种网络架构。实验证明了该网络在 HCInew 数据集上的有效性,尤其是在处理遮挡和细节区域方面。
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来源期刊
IET Computer Vision
IET Computer Vision 工程技术-工程:电子与电气
CiteScore
3.30
自引率
11.80%
发文量
76
审稿时长
3.4 months
期刊介绍: IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision. IET Computer Vision welcomes submissions on the following topics: Biologically and perceptually motivated approaches to low level vision (feature detection, etc.); Perceptual grouping and organisation Representation, analysis and matching of 2D and 3D shape Shape-from-X Object recognition Image understanding Learning with visual inputs Motion analysis and object tracking Multiview scene analysis Cognitive approaches in low, mid and high level vision Control in visual systems Colour, reflectance and light Statistical and probabilistic models Face and gesture Surveillance Biometrics and security Robotics Vehicle guidance Automatic model aquisition Medical image analysis and understanding Aerial scene analysis and remote sensing Deep learning models in computer vision Both methodological and applications orientated papers are welcome. Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review. Special Issues Current Call for Papers: Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf
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