Colorful 3D reconstruction at high resolution using multi-view representation

Zheng, Yunfei; Zeng, Guang; Li, Haisheng; Cai, Qiang; Du, Junping

doi:10.1016/j.jvcir.2022.103486

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…different objects [12]. Deep learning methods can better model the variations in shape, texture, and illumination of 3D objects [13]. The field of deep learning-based image synthesis has made remarkable progress in recent years, aided by the availability of more open source datasets [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Huang

Yao

et al. 2023

PhotoniX

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Super-resolution optical imaging is crucial to the study of cellular processes. Current super-resolution fluorescence microscopy is restricted by the need of special fluorophores or sophisticated optical systems, or long acquisition and computational times. In this work, we present a deep-learning-based super-resolution technique of confocal microscopy. We devise a two-channel attention network (TCAN), which takes advantage of both spatial representations and frequency contents to learn a more precise mapping from low-resolution images to high-resolution ones. This scheme is robust against changes in the pixel size and the imaging setup, enabling the optimal model to generalize to different fluorescence microscopy modalities unseen in the training set. Our algorithm is validated on diverse biological structures and dual-color confocal images of actin-microtubules, improving the resolution from ~ 230 nm to ~ 110 nm. Last but not least, we demonstrate live-cell super-resolution imaging by revealing the detailed structures and dynamic instability of microtubules.

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Section: Introductionmentioning

confidence: 99%

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Huang

Yao

et al. 2023

PhotoniX

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“…In addition, the computation time of FaCO itself proposed in this paper increases as the computation scale increases. Therefore, it is also of great significance to pay more attention to the spatio-temporal dimension of the search path [ 36 ] and to optimize the computational performance of the algorithm itself and the load-balancing problem of parallel computation of the graph dissection process.…”

Section: Future Workmentioning

confidence: 99%

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

Sun

et al. 2022

Biomimetics

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With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.

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“…These applications require rapid and accurate 3D digitization of real scenes to meet huge field needs. The methods for reconstructing 3D models can usually be divided into active and passive methods [7][8][9]. Multi view Iterative reconstruction is a passive reconstruction method that does not require physical contact with the reconstruction object.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Combined Techniques for Interval-Valued Intuitionistic Fuzzy Multiple-Attribute Group Decision-Making and Applications to Quality Evaluation of Large-Scale Multi-View 3D Reconstruction

Bian,

Che

2023

IEEE Access

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With the widespread application of computer vision in many fields, quickly and accurately obtaining the three-dimensional information covered by the target object has become a concern in the field of vision research. Large scale multi view Iterative reconstruction refers to obtaining multiple images of the target object through the camera, then obtaining the corresponding relationship between points and spatial mapping relationship between each image through the pixel information on the image, and then obtaining the 3D information of the target object through the relationship between points and space, finally completing the multi view Iterative reconstruction of the target object. Multi view Iterative reconstruction has a wide range of applications, and has very important applications in medical treatment, industrial detection, space technology, etc. Compared with binocular or multi camera stereo vision, monocular stereo vision has obvious advantages in terms of cost, structure and the way to obtain pictures. Multi view Iterative reconstruction of objects can be completed by processing multiple images to achieve the effect of multi camera stereo vision. The quality evaluation of large-scale multi-view 3D reconstruction is the MAGDM. Recently, the TODIM and EDAS technique has been employed to manage MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a useful tool for portraying uncertain information during the quality evaluation of large-scale multi-view 3D reconstruction. In this paper, the interval-valued intuitionistic fuzzy TODIM-EDAS (IVIF-TODIM-EDAS) technique is built to manage the MAGDM under IVIFSs. At last, the numerical example for quality evaluation of large-scale multi-view 3D reconstruction is employed to show the IVIF-TODIM-EDAS decision technique. The main contribution of this paper is outlined: (1) the TODIM technique based on EDAS has been extended to IVIFSs based on Entropy technique; (2) the Entropy technique is employed to manage weight based on score values under IVIFSs. (3) the IVIF-TODIM-EDAS technique is founded to manage the MAGDM under IVIFSs; (4) a numerical example for quality evaluation of large-scale multi-view 3D reconstruction and some comparative analysis is supplied to verify the proposed technique. INDEX TERMS: Multiple-attribute group decision-making (MAGDM); interval-valued intuitionistic fuzzy sets (IVIFSs); TODIM technique; EDAS technique; large-scale multi-view 3D reconstruction

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Colorful 3D reconstruction at high resolution using multi-view representation

Cited by 17 publications

References 15 publications

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

Enhanced Combined Techniques for Interval-Valued Intuitionistic Fuzzy Multiple-Attribute Group Decision-Making and Applications to Quality Evaluation of Large-Scale Multi-View 3D Reconstruction

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