EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism

Gao, Ming; Hu, Deng; Xiang, Sen; Jin, Wanqin; He, Z. X.

doi:10.3390/s22166291

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Gao et al [30] presented for accurately estimating depth in (LF) images by calculating the slope of EPIs through a directional relationship model and attention mechanism. The proposed algorithm's effectiveness is demonstrated through outperforming existing algorithms and its potential for use in various LF-related applications, including 3D reconstruction, target detection, and tracking.…”

Section: Related Workmentioning

confidence: 99%

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

Imtiaz,

Kwon,

Hossain

et al. 2023

CSSE

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This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing. A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature from the directional sub-aperture LF data cube. For the enhancement of the depth map, dual iteration-based weighted median filtering (WMF) is used to reduce surface noise and enhance the accuracy of the reconstruction. Generating a 3D point cloud involves combining two key elements: the enhanced depth map and the central view of the light field image. The proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing (HCI) and real-world LFM datasets. The results are compared with different state-of-the-art methods. The structural similarity index (SSIM) gain for boxes, cotton, pillow, and pens are 0.9760, 0.9806, 0.9940, and 0.9907, respectively. Moreover, the discrete entropy (DE) value for LFM depth maps exhibited better performance than other existing methods.

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Section: Related Workmentioning

confidence: 99%

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

Imtiaz,

Kwon,

Hossain

et al. 2023

CSSE

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“…Unlike conventional imaging, which captures the 2D projection of light rays, LF imaging collects data with many dimensions [ 1 ]. This abundance of visual information in LF pictures, in addition to their immersive description of the real world, may help several image processing and computer vision tasks, such as depth estimation [ 2 , 3 ], de-occlusion [ 4 , 5 ], salient object detection [ 6 , 7 ], and image post-refocus [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Light Field Image Super-Resolution Using Deep Residual Networks on Lenslet Images

Salem

Ibrahem

Kang

2023

Sensors

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Due to its widespread usage in many applications, numerous deep learning algorithms have been proposed to overcome Light Field’s trade-off (LF). The sensor’s low resolution limits angular and spatial resolution, which causes this trade-off. The proposed method should be able to model the non-local properties of the 4D LF data fully to mitigate this problem. Therefore, this paper proposes a different approach to increase spatial and angular information interaction for LF image super-resolution (SR). We achieved this by processing the LF Sub-Aperture Images (SAI) independently to extract the spatial information and the LF Macro-Pixel Image (MPI) to extract the angular information. The MPI or Lenslet LF image is characterized by its ability to integrate more complementary information between different viewpoints (SAIs). In particular, we extract initial features and then process MAI and SAIs alternately to incorporate angular and spatial information. Finally, the interacted features are added to the initial extracted features to reconstruct the final output. We trained the proposed network to minimize the sum of absolute errors between low-resolution (LR) input and high-resolution (HR) output images. Experimental results prove the high performance of our proposed method over the state-of-the-art methods on LFSR for small baseline LF images.

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Light field depth estimation: A comprehensive survey from principles to future

Wang,

Sheng,

Chen

et al. 2024

High-Confidence Computing

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EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism

Cited by 4 publications

References 36 publications

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

Light Field Image Super-Resolution Using Deep Residual Networks on Lenslet Images

Light field depth estimation: A comprehensive survey from principles to future

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