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Symmetry
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Symmetry in Probablistic Models and Aerospace Systems
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Symmetry in Probablistic Models and Aerospace Systems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2210

Special Issue Editors

Dr. Yuankai Li

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: adaptive control; stochastic control; intelligent control; decision control; optimal filtering; Bayesian estimation; statistical learning; swarm intelligence; target tracking; task planning; aerospace systems; space operation; autonomous unmanned systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yong Wang

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
Interests: machine learning; aerospace systems; autonomous unmanned systems; visual tracking; deep learning; pose estimation; object detection; information fusion; swarm intelligence; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Shen

E-Mail Website
Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Interests: information fusion; target tracking; aerospace systems
Prof. Dr. Di Wu

E-Mail Website
Guest Editor
College of Computer and Information Science, Southwest University, Chongqing, China
Interests: big data; recommender systems; environmental protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, probalistic models play a significant role in information sciences and aerospace operation technologies. To some extent, improving the intelligence of aerospace systems benefits from fast-developing probabilistic modeling approaches, including Bayesion estimation, statistical inference, data mining, machine learning, Gaussian process regression, random matrix, stochastic optimization, Monte Carlo simulation, etc., thus promoting technique emergence in aerospace science and engineering.

This Special Issue is devoted to recent advances in probablistic models related to the analysis and use of symmetries in multidisplinary areas of aerospace systems. The Special Issue aims to provide a way for people studying in mathematics, information science, system engineering, or other related fields to disseminate their findings about symmetries. Papers on topics of interest, including but not limited to the listed keywords, are solicited.

Dr. Yuankai Li
Dr. Yong Wang
Dr. Kai Shen
Prof. Dr. Di Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Symmetry is an international peer-reviewed open access monthly 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

  • symmetry
  • bayes filters
  • statistical learning
  • intelligent computing
  • stochastic signal processing
  • optimization and robustness
  • recognition and tracking
  • information fusion
  • guidance, navigation, and control
  • trajectory planning
  • swarm intelligence
  • robotics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

17 pages, 4088 KiB  
Article
Enhanced LDR Detail Rendering for HDR Fusion by TransU-Fusion Network
by Bo Song, Rui Gao, Yong Wang and Qi Yu
Symmetry 2023, 15(7), 1463; https://doi.org/10.3390/sym15071463 - 23 Jul 2023
Cited by 2 | Viewed by 1422
Abstract
High Dynamic Range (HDR) images are widely used in automotive, aerospace, AI, and other fields but are limited by the maximum dynamic range of a single data acquisition using CMOS image sensors. High dynamic range images are usually synthesized through multiple exposure techniques [...] Read more.
High Dynamic Range (HDR) images are widely used in automotive, aerospace, AI, and other fields but are limited by the maximum dynamic range of a single data acquisition using CMOS image sensors. High dynamic range images are usually synthesized through multiple exposure techniques and image processing techniques. One of the most challenging task in multiframe Low Dynamic Range (LDR) images fusion for HDR is to eliminate ghosting artifacts caused by motion. In traditional algorithms, optical flow is generally used to align dynamic scenes before image fusion, which can achieve good results in cases of small-scale motion scenes but causes obvious ghosting artifacts when motion magnitude is large. Recently, attention mechanisms have been introduced during the alignment stage to enhance the network’s ability to remove ghosts. However, significant ghosting artifacts still occur in some scenarios with large-scale motion or oversaturated areas. We proposea novel Distilled Feature TransformerBlock (DFTB) structure to distill and re-extract information from deep image features obtained after U-Net downsampling, achieving ghost removal at the semantic level for HDR fusion. We introduce a Feature Distillation Transformer Block (FDTB), based on the Swin-Transformer and RFDB structure. FDTB uses multiple distillation connections to learn more discriminative feature representations. For the multiexposure moving scene image fusion HDR ghost removal task, in the previous method, the use of deep learning to remove the ghost effect in the composite image has been perfect, and it is almost difficult to observe the ghost residue of moving objects in the composite HDR image. The method in this paper focuses more on how to save the details of LDR image more completely after removing the ghost to synthesize high-quality HDR image. After using the proposed FDTB, the edge texture details of the synthesized HDR image are saved more perfectly, which shows that FDTB has a better effect in saving the details of image fusion. Futhermore, we propose a new depth framework based on DFTB for fusing and removing ghosts from deep image features, called TransU-Fusion. First of all, we use the encoder in U-Net to extract image features of different exposures and map them to different dimensional feature spaces. By utilizing the symmetry of the U-Net structure, we can ultimately output these feature images as original size HDR images. Then, we further fuse high-dimensional space features using Dilated Residual Dense Block (DRDB) to expand the receptive field, which is beneficial for repairing over-saturated regions. We use the transformer in DFTB to perform low-pass filtering on low-dimensional space features and interact with global information to remove ghosts. Finally, the processed features are merged and output as an HDR image without ghosting artifacts through the decoder. After testing on datasets and comparing with benchmark and state-of-the-art models, the results demonstrate our model’s excellent information fusion ability and stronger ghost removal capability. Full article
(This article belongs to the Special Issue Symmetry in Probablistic Models and Aerospace Systems)
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