Analyzing Image Focus using Deep Neural Network for 3D Shape Recovery

Li, Mikhail; Mutahira, Husna; Bazzi, Ahmad; Muhammad, Mannan Saeed

doi:10.1109/intellect47034.2019.8955454

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…By utilizing the relationship between image blur level and depth, it is possible to estimate the object. The depth value of each part, and finally, the 3D reconstruction, convert the depth information into 3D coordinates, thereby obtaining the 3D reconstruction model of the object [78][79][80][81][82]. Yan et al [83] used the multi-directional modified Laplacian operator to map the depth maps corresponding to different focal points and employed an iterative edge repair method to refine the reconstruction results.…”

Section: Shape From Focusmentioning

confidence: 99%

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

Zhou,

Wu,

Zuo

et al. 2024

Sensors

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With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine. The rapid development of 3D reconstruction is becoming inevitable. This survey categorizes the various methods and technologies used in 3D reconstruction. It explores and classifies them based on three aspects: traditional static, dynamic, and machine learning. Furthermore, it compares and discusses these methods. At the end of the survey, which includes a detailed analysis of the trends and challenges in 3D reconstruction development, we aim to provide a comprehensive introduction for individuals who are currently engaged in or planning to conduct research on 3D reconstruction. Our goal is to help them gain a comprehensive understanding of the relevant knowledge related to 3D reconstruction.

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Section: Shape From Focusmentioning

confidence: 99%

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

Zhou,

Wu,

Zuo

et al. 2024

Sensors

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“…Furthermore, in order to recover a proper 3D shape of the object from the obtained initial shape, it is required to apply surface approximation techniques which are also computationally expensive (Li, Mutahira, Ahmad, and Muhammad (2019a); Li, Mutahira, Ahmad, and Muhammad (2019b)).…”

Section: Shape Aggregation and Deep Neural Networkmentioning

confidence: 99%

A simplified approach using deep neural network for fast and accurate shape from focus

Mutahira

Muhammad

et al. 2020

Microscopy Res & Technique

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Three-dimensional shape recovery is an important issue in the field of computer vision. Shape from Focus (SFF) is one of the passive techniques that uses focus information to estimate the three-dimensional shape of an object in the scene. Images are taken at multiple positions along the optical axis of the imaging device and are stored in a stack. In order to reconstruct the three dimensional shape of the object, the best-focused positions are acquired by maximizing the focus curves obtained via application of a focus measure operator. In this article, Deep Neural Network (DNN) is employed to extract the more accurate depth of each object point in the image stack. The size of each image in the stack is first reduced and then provided to the proposed DNN network to aggregate the shape. The initial shape is refined by applying a median filter, and later the reconstructed shape is sized back to original by utilizing bi-linear interpolation. The results are compared with commonly used focus measure operators by employing root mean squared error (RMSE), correlation, and image quality index (Q). Compared to other methods, the proposed SFF method using DNN shows higher precision and low computational time consumption.

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Region of Interest Selection-Based Autofocusing for High Magnification Systems

Helmy,

Choi

2023

IEEE Trans. Comput. Imaging

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Analyzing Image Focus using Deep Neural Network for 3D Shape Recovery

Cited by 3 publications

References 9 publications

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

A simplified approach using deep neural network for fast and accurate shape from focus

Region of Interest Selection-Based Autofocusing for High Magnification Systems

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