Deep Learning and Its Applications in Image Reconstruction
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 2586
Special Issue Editors
Interests: 3D shape recovery from image focus; computer vision; pattern recognition and machine learning
Special Issue Information
Dear Colleagues,
Three-dimensional shape reconstruction from single or multiple images acquired using a single or multiple cameras is a classical problem in computer vision that has been approached via many techniques. Despite great progress achieved in 3D reconstruction from acquired data, accurate depth map recovery is still a big challenge. Deep learning techniques have attracted many researchers to the computer vision field to solve problems such as image segmentation, object detection, and recognition. This success has also led to the implementation of deep learning techniques for 3D reconstruction. Deep neural networks are especially suitable because they are able to encode rich prior information about the space of 3D shapes, which helps to resolve ambiguities. These models can learn a diverse type of features to ultimately allow robust 3D reconstruction. However, deep learning techniques for 3D reconstruction are still in their initial phase. It is anticipated that deep-learning-based methods can improve accuracy in 3D reconstruction from single or multiple images.
The purpose of this Special Issue is to disseminate original research papers or state-of-the-art surveys that pertain to novel or emerging techniques and applications in the field of computer vision. Papers may contribute to application areas that have emerged during the past decade or may relate to new subdomains of long-standing applications. Submissions are particularly welcome in, though not limited to, the areas in the list of keywords below.
We look forward to receiving your contributions, and to future collaborations.
Thank you for your cooperation.
Dr. Muhammad Tariq Mahmood
Dr. Usman Ali
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- computer vision
- 3D reconstruction
- depth estimation
- deep learning
- energy minimization and optimization
- generative adversarial networks
- reinforcement learning
- applications of 3D vision
- RGB-D cameras
