All-In-Focus Synthetic Aperture Imaging

Yang, Tao; Zhang, Yanning; Yu, Jingyi; Li, Jing; Ma, Wenguang; Tong, Xiaomin; Yu, Rui; Ran, Lingyan

doi:10.1007/978-3-319-10599-4_1

Cited by 30 publications

(29 citation statements)

References 14 publications

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“…b: YANG et al [35] In order to reconstruct the whole scene through occlusion, they present a synthetic aperture imaging method. This method is proposed base on light field visibility analysis.…”

Section: The Performance Comparison 1) Comparison Methodsmentioning

confidence: 99%

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Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback

Dai

et al. 2019

IEEE Access

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Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback. The semantic content we concerned for better de-occluded imaging is the foreground occlusions rather than the whole scene. Therefore, unlike other methods worked on pixel-level, we start from semantic layer and present a semantic labeling method based on feedback. Semantic labeling map deeply mines visual data in synthetic image and preserves the semantic information of foreground occluder. On the basis of semantic feedback strategy, semantic labeling map will conversely pass to synthetic imaging process. The proposed data-driven variable synthetic aperture imaging contains two levels: one is adaptive changeable imaging aperture driven by synthetic depth and perspective angle, the other is light ray screening driven by visual information in semantic labeling map. On this basis, the hybrid camera view and superimposition of foreground occlusion can be removed. Evaluations on several complex indoor scenes and real outdoor environments demonstrate the superiority and robustness performance of our proposed approach. INDEX TERMS Synthetic aperture imaging, data-driven variable synthetic aperture, semantic feedback imaging, multi-camera array.

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Section: The Performance Comparison 1) Comparison Methodsmentioning

confidence: 99%

“…In this section, we compare our approach with traditional synthetic aperture imaging [34], Yang et al's method [35] and Pei et al's method [29] on UCSD light field database. All data are collected outdoors with high crowded degree.…”

Section: ) Experiments On Ucsd Light Field Databasementioning

confidence: 99%

Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback

Dai

et al. 2019

IEEE Access

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“…The cost functions, however, may fail under severe occlusions. Yang et al [154] divided the scene into multiple visibility layers, and then propagated the visibility information among those layers using an optimization framework. Their approach was able to produce a synthetic aperture image with a wider DOF compared with previous works.…”

Section: Synthetic Aperture Imagingmentioning

confidence: 99%

Light Field Image Processing: An Overview

Masiá

Jarabo

et al. 2017

IEEE J. Sel. Top. Signal Process.

459

215

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Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher-dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics and signal processing communities. In this article, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data. Index Terms-Light field imaging, light field processing.

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“…Synthetic apertures (SA) find applications in many fields, such as radar [1][2][3], radio telescopes [4,5], microscopy [6], sonar [7,8], ultrasound [9,10], LiDAR [11,12], and optical imaging [13][14][15][16][17][18][19][20]. They approximate the signal of a single hypothetical wide aperture sensor with either an array of static small-aperture sensors or a single-moving small aperture sensor whose individual signals are computationally combined to increase resolution, depth-of-field, frame rate, contrast, and signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Thermal Airborne Optical Sectioning

2019

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We apply a multi-spectral (RGB and thermal) camera drone for synthetic aperture imaging to computationally remove occluding vegetation for revealing hidden objects, as required in archeology, search-and-rescue, animal inspection, and border control applications. The radiated heat signal of strongly occluded targets, such as a human bodies hidden in dense shrub, can be made visible by integrating multiple thermal recordings from slightly different perspectives, while being entirely invisible in RGB recordings or unidentifiable in single thermal images. We collect bits of heat radiation through the occluder volume over a wide synthetic aperture range and computationally combine them to a clear image. This requires precise estimation of the drone's position and orientation for each capturing pose, which is supported by applying computer vision algorithms on the high resolution RGB images.

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All-In-Focus Synthetic Aperture Imaging

Cited by 30 publications

References 14 publications

Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback

Data-Driven Variable Synthetic Aperture Imaging Based on Semantic Feedback

Light Field Image Processing: An Overview

Thermal Airborne Optical Sectioning

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