Xin Zheng , Boyang Wang , Deyang Liu , Chengtao Lv , Jiebin Yan , Ping An
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引用次数: 0
Abstract
Light-field salient object detection (SOD) has become an emerging trend as it records comprehensive information about natural scenes that can benefit salient object detection in various ways. However, salient object detection models with light-field data as input have not been thoroughly explored. The existing methods cannot effectively suppress the noise, and it is difficult to distinguish the foreground and background under challenging conditions including self-similarity, complex backgrounds, large depth of field, and non-Lambertian scenarios. In order to extract the feature of light-field images effectively and suppress the noise in light-field, in this paper, we propose a foreground and context dual guided network. Specifically, we design a global context extraction module (GCEM) and a local foreground extraction module (LFEM). GCEM is used to suppress global noise and roughly predict saliency maps. GCEM also can extract global context information from deep-level features to guide decoding process. By extracting local information from shallow-level, LFEM refines the prediction obtained by GCEM. In addition, we use RGB images to enhance the light-field images before the input GCEM. Experimental results show that our proposed method is effective in suppressing global noise and achieves better results when dealing with transparent objects and complex backgrounds. The experimental results show that the proposed method outperforms several other state-of-the-art methods on three light-field datasets.
期刊介绍:
Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following:
To present a forum for the advancement of theory and practice of image communication.
To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems.
To contribute to a rapid information exchange between the industrial and academic environments.
The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world.
Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments.
Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.