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Symmetry
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Symmetry and Asymmetry in Computer Vision Under Extreme Environments
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Symmetry and Asymmetry in Computer Vision Under Extreme Environments

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1501

Special Issue Editor

Dr. Peng Liu

E-Mail Website
Guest Editor
Computing Center, Ocean University of China, Qingdao 266100, China
Interests: image processing; computer vision; deep learning

Special Issue Information

Dear Colleagues,

The Special Issue on "Symmetry and Asymmetry in Computer Vision Under Extreme Environments" addresses the unique challenges and innovations of computer vision when applied to extreme conditions, such as underwater environments, extreme weather, and low-light situations. These conditions significantly impact visibility and image quality, necessitating advanced techniques in image processing and various computer vision tasks.

This Special Issue aims to explore the roles of symmetry and asymmetry in overcoming these challenges. We welcome original research articles and reviews in this Special Issue. Research areas may include (but are not limited to) the following:

  1. Imaging and Sensing Techniques: Advanced optical, sonar, and hyperspectral methods to enhance visibility and clarity in extreme environments.
  2. Image Processing and Enhancement: Development of algorithms for improving image quality, color correction, and contrast enhancement to mitigate the effects of light absorption and scattering.
  3. Object Detection and Classification: Application of machine learning and deep learning techniques for detecting and classifying objects in challenging conditions.
  4. Vision Systems for Autonomous Vehicles: Exploration of vision systems used in autonomous vehicles and autonomous underwater vehicles (AUVs), focusing on real-time image processing and navigation in extreme environments.

By addressing the technical challenges posed by these extreme conditions and investigating innovative solutions, this Special Issue seeks to advance the field of computer vision and enhance our ability to perceive and understand complex visual landscapes.

I look forward to receiving your contributions.

Dr. Peng Liu
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • underwater vision
  • object detection, tracking, and recognition
  • machine learning and deep learning
  • medical image processing and analysis
  • 3D vision and reconstruction
  • image enhancement
  • image restoration
  • pattern recognition
  • symmetry
  • asymmetry

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

Published Papers (2 papers)

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Research

31 pages, 4842 KB  
Article
FDR-Net: Fine-Grained Lesion Detection Model for Tilapia in Aquaculture via Multi-Scale Feature Enhancement and Spatial Attention Fusion
by Chenhui Zhou and Vladimir Y. Mariano
Symmetry 2026, 18(4), 598; https://doi.org/10.3390/sym18040598 - 31 Mar 2026
Cited by 1 | Viewed by 459
Abstract
In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle symptoms, polymorphic lesions, and high susceptibility to environmental perturbations such [...] Read more.
In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle symptoms, polymorphic lesions, and high susceptibility to environmental perturbations such as water turbidity and illumination fluctuations. Existing detection models generally suffer from inadequate lightweight design, poor fine-grained lesion feature extraction, and deficient adaptability to class imbalance, failing to meet the stringent requirements of precise diagnosis in real-world aquaculture scenarios. To address these challenges, this study proposes FDR-Net: a fine-grained lesion detection model for tilapia via multi-scale feature enhancement and spatial attention fusion. Using image data of Nile tilapia (Oreochromis niloticus) covering 6 common diseases and healthy individuals (from the NTD-1 dataset), the model incorporates symmetry-aware design logic, leveraging the morphological and textural symmetry of healthy tilapia tissues to capture lesion-induced symmetry-breaking features, thereby improving fine-grained lesion detection accuracy. Through depth-width scaling coefficients, FDR-Net achieves lightweight optimization while integrating three core modules and a task-specific loss function for full-chain optimization: specifically, a Micro-lesion Feature Enhancement Module (MLFEM) is embedded in key feature layers of the backbone network to accurately extract edge and texture features of incipient fine-grained lesions via multi-scale frequency decomposition and residual fusion; subsequently, a Lightweight Multi-scale Position Attention Module (MS_PSA) and a Single-modal Intra-feature Contrastive Fusion Module (SMICFM) are collaboratively deployed—the former focusing on spatial localization of lesion features, and the latter enhancing lesion-background discriminability through channel-spatial feature recalibration and contrastive fusion; finally, a Class-Aware Weighted Hybrid Loss (CAWHL) function is combined with customized small-target anchor boxes to alleviate class imbalance and further improve localization and classification accuracy of fine-grained lesions. Empirical evaluations on the NTD-1 dataset demonstrate that compared with mainstream state-of-the-art baseline models, FDR-Net achieves a peak recognition accuracy of 90.1% with substantially enhanced mAP50-95 performance. Retaining lightweight characteristics, it exhibits superior performance in identifying incipient fine-grained lesions and strong adaptability to simulated complex aquaculture scenarios. Collectively, this study provides an efficient technical backbone for the rapid and precise detection of tilapia fine-grained lesions, offering a potential solution for precise disease management in tilapia farming. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computer Vision Under Extreme Environments)
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25 pages, 31807 KB  
Article
United Scattering Transmission Model for Haze Removal
by Zhengfei Wang, Rui Wang, Anran Li and Tingting Ji
Symmetry 2026, 18(3), 472; https://doi.org/10.3390/sym18030472 - 10 Mar 2026
Viewed by 376
Abstract
Haze removal methods based on the estimation of scene depth ratio in the Atmospheric Scattering Model (ASM) have achieved satisfactory results. However, the ASM ignores the blur equivalent to a point spread function caused by forward scattering. This paper proposes a simplified United [...] Read more.
Haze removal methods based on the estimation of scene depth ratio in the Atmospheric Scattering Model (ASM) have achieved satisfactory results. However, the ASM ignores the blur equivalent to a point spread function caused by forward scattering. This paper proposes a simplified United Scattering Transmission Model (USTM), in which both forward scattering and back scattering are taken into consideration physically. It utilizes Taylor expansion to correlate the hazy image and its second-order operator with the dehazed image. Additionally, we establish a layered decomposition mechanism of the scattering medium; by fitting the limitation expression and the image signal at infinity, the parameters related to the inherent optical properties used in the model can be obtained. When the stable transmittance estimation approaches are applied into this USTM, the scene radiance can be restored effectively. We conducted evaluation experiments on datasets including RESIDE-RTTS (Real-world Task-Driven Testing Set), Haze4K, and DenseHaze, using metrics such as PSNR, SSIM, newly visible edges and the ratio of the gradients. The results demonstrate that USTM achieves satisfactory results across multiple evaluation dimensions. Regarding the core objective fidelity metric PSNR, it achieves an optimal score of 11.87 dB, representing an approximate 3.85% improvement over the second-best method. Compared to the traditional ASM, the USTM shows an average improvement of approximately 23.5% in edge restoration capability (newly visible edges) and an average improvement of approximately 18.1% in gradient fidelity (the mean ratio of the gradients). Furthermore, compared with advanced deep learning dehazing methods, our method remains highly competitive in edge and gradient restoration metrics, and its lightweight design provides excellent efficiency and compatibility with downstream tasks. The comprehensive results show that the USTM achieves effective improvements in both physical accuracy and detail restoration performance. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Computer Vision Under Extreme Environments)
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