Light Field Image Processing: An Overview

Wu, Gaochang; Masiá, Belén; Jarabo, Adrián; Zhang, Yuchen; Wang, Liangyong; Dai, Qionghai; Chai, Tianyou; Liu, Yebin

doi:10.1109/jstsp.2017.2747126

Cited by 435 publications

(208 citation statements)

References 245 publications

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“…Moreover, they can be incorporated into organic electronics as flexible substrates or dielectric. [55][56][57][58] For example, Bao and Bettinger have fabricated OFETs based on poly (l-lactide-co-glycolide) (PLGA), whose chemical structure can be found in Figure 3a. [55] Interestingly, these devices presented water-stable behavior through Reproduced with permission.…”

Section: Polylactide and Poly(lactide-co-glycolide)mentioning

confidence: 99%

Integration of Biomaterials into Sensors Based on Organic Thin‐Film Transistors

Zhou

Huang

2018

Macromol. Rapid Commun.

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Sensors based on organic thin-film transistors (OTFTs) present various advantages, including high sensitivity and mechanical flexibility, thus possessing potential applications such as wearable devices and biomedical electronics for health monitoring, etc. However, such applications are partially limited by the biocompatibility, biodegradability, and sensitivity to target analytes of OTFT-based sensors, which can be improved by the incorporation of diverse biomaterials. This article presents a brief review from the viewpoint of the type of the integrated biomaterials, including naturally occurring biomacromolecules such as proteins, enzymes, and deoxyribonucleic acid, as well as biocompatible polymers such as polylactide, poly(lactide-co-glycolide), poly(ethylene glycol), cellulose, polydimethylsiloxane, parylene, etc. It is believed that future work in this field should be devoted to the selectivity, sensitivity, and stability improvement as well as the high-level integration and sophistication on the basis of the OTFT-based sensors for physical, chemical, and biological sensing applications.

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Section: Polylactide and Poly(lactide-co-glycolide)mentioning

confidence: 99%

Integration of Biomaterials into Sensors Based on Organic Thin‐Film Transistors

Zhou

Huang

2018

Macromol. Rapid Commun.

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“…In this study, we propose a new method for reconstructing detailed 3D skin depth information using a low‐resolution light field camera (Lytro camera, 1st generation, Mountain View, CA, USA), which has not been used to reconstruct the 3D skin depth information required for haptic palpation, thus far. Light field technology uses a main lens and a micro‐lens array to collect angular information that could not be taken into account in conventional photography, allowing the acquisition of light rays in all directions . A light field camera can acquire both spatial and angular information and produce sub‐aperture images from multiple viewpoints.…”

Section: Introductionmentioning

confidence: 99%

“…Light field technology uses a main lens and a micro-lens array to collect angular information that could not be taken into account in conventional photography, allowing the acquisition of light rays in all directions. 16 A light field camera can acquire both spatial and angular information and produce sub-aperture images from multiple viewpoints. Therefore, it is possible to compute depth information of the object more accurately and easily from a single shoot.…”

Section: Introductionmentioning

confidence: 99%

Accurate depth estimation of skin surface using a light‐field camera toward dynamic haptic palpation

Kim

2019

Skin Research and Technology

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Background Haptic skin palpation with three‐dimensional skin surface reconstruction from in vivo skin images in order to acquire both tactile and visual information has been receiving much attention. However, the depth estimation of skin surface, using a light field camera that creates multiple images with a micro‐lens array, is a difficult problem due to low‐resolution images resulting in erroneous disparity matching. Methods Multiple low‐resolution images decoded from a light field camera have limitations to accurate 3D surface reconstruction needed for haptic palpation. To overcome this, a deep learning method, Generative Adversarial Networks, was employed to generate super‐resolved skin images that preserve surface detail without blurring, and then, accurate skin depth was estimated by taking multiple subsequent steps including lens distortion correction, sub‐pixel shifted image generation using phase shift theorem, cost‐volume building, multi‐label optimization, and hole filling and refinement, which is a new approach for 3D skin surface reconstruction. Results Experimental results of the deep‐learning‐based super‐resolution method demonstrated that the textural detail (wrinkles) of super‐resolved skin images is well preserved, unlike other super‐resolution methods. In addition, the depth maps computed with our proposed algorithm verify that our method can produce more accurate and robust results compared to other state‐of‐the‐art depth map computation methods. Conclusion Herein, we first proposed depth map estimation of skin surfaces using a light field camera and subsequently tested it with several skin images. The experimental results established the superiority of the proposed scheme.

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“…D ENSELY-SAMPLED Light Field (DSLF) is a discrete representation of the 4D approximation of the plenoptic function parameterized by two parallel planes (camera plane and image plane) [1], where multi-perspective camera views are arranged in such a way that the disparity ranges between adjacent views are less than or equal to one pixel [2]. DSLF has a wide range of applications, such as depth estimation, super-resolution and synthetic aperture imaging [3], visualization on 3DTV [4] and Virtual Reality (VR) [5] devices. In realworld environments, a DSLF is extremely difficult to capture by modern light field acquisition systems, such as microlens array (MLA) [6,7], multi-camera array [8]- [10] and coded mask [11,12].…”

Section: Introductionmentioning

confidence: 99%

Fast: Flow-Assisted Shearlet Transform for Densely-Sampled Light Field Reconstruction

Gao

Koch

Bregović

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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The Image-Based Rendering (IBR) approach using Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction. The ST-based DSLF reconstruction typically relies on an iterative thresholding algorithm for Epipolar-Plane Image (EPI) sparse regularization in shearlet domain, involving dozens of transformations between image domain and shearlet domain, which are in general time-consuming. To overcome this limitation, a novel learning-based ST approach, referred to as Deep Residual Shearlet Transform (DRST), is proposed in this paper. Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain. The DRST network is trained on synthetic Sparsely-Sampled Light Field (SSLF) data only by leveraging elaborately-designed masks. Experimental results on three challenging real-world light field evaluation datasets with varying moderate disparity ranges (8 -16 pixels) demonstrate the superiority of the proposed learning-based DRST approach over the non-learning-based ST method for DSLF reconstruction. Moreover, DRST provides a 2.4x speedup over ST, at least.Index Terms-Densely-sampled light field reconstruction, novel view synthesis, epipolar-plane image, Shearlet Transform (ST), Deep Residual Shearlet Transform (DRST).• We propose a learning-based DRST method that achieves better DSLF reconstruction performance than the nonlearning-based ST algorithm on three evaluation datasets arXiv:2003.08865v1 [cs.MM]

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Light Field Image Processing: An Overview

Cited by 435 publications

References 245 publications

Integration of Biomaterials into Sensors Based on Organic Thin‐Film Transistors

Integration of Biomaterials into Sensors Based on Organic Thin‐Film Transistors

Accurate depth estimation of skin surface using a light‐field camera toward dynamic haptic palpation

Fast: Flow-Assisted Shearlet Transform for Densely-Sampled Light Field Reconstruction

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