Emerging Deep Learning Models and Applications in Image Processing and Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 214

Special Issue Editor


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Guest Editor
Department of Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea
Interests: deep learning; generative adversarial networks; learning with noisy labels; multi-task learning; image processing; computer vision

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to gather articles that address new and existing challenges in image processing and computer vision using advanced deep learning algorithms and models. We are particularly interested in approaches that push the boundaries of traditional image analysis by leveraging innovative deep learning techniques. Studies that integrate hybrid methods, combining deep learning with other machine learning or optimization techniques, are also highly encouraged. Furthermore, applications that use novel deep neural network architectures and frameworks—such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models—for tasks like image recognition, object detection, segmentation, anomaly detection, 3D Gaussian splatting, and 3D image reconstruction, would be highly relevant to this Special Issue. We welcome a wide range of contributions, especially those demonstrating practical applications, theoretical advancements, or novel implementations in real-world computer vision tasks.

Dr. Kyeongbo Kong
Guest Editor

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Keywords

  • image recognition
  • object detection
  • image segmentation
  • image completion
  • anomaly detection
  • 3D Gaussian splatting
  • 3D image reconstruction
  • hybrid deep learning approaches
  • real-time visual recognition

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Published Papers (1 paper)

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Research

21 pages, 6925 KB  
Article
U2-LFOR: A Two-Stage U2 Network for Light-Field Occlusion Removal
by Mostafa Farouk Senussi, Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud and Hyun-Soo Kang
Mathematics 2025, 13(17), 2748; https://doi.org/10.3390/math13172748 (registering DOI) - 26 Aug 2025
Abstract
Light-field (LF) imaging transforms occlusion removal by using multiview data to reconstruct hidden regions, overcoming the limitations of single-view methods. However, this advanced capability often comes at the cost of increased computational complexity. To overcome this, we propose the U2-LFOR network, [...] Read more.
Light-field (LF) imaging transforms occlusion removal by using multiview data to reconstruct hidden regions, overcoming the limitations of single-view methods. However, this advanced capability often comes at the cost of increased computational complexity. To overcome this, we propose the U2-LFOR network, an end-to-end neural network designed to remove occlusions in LF images without compromising performance, addressing the inherent complexity of LF imaging while ensuring practical applicability. The architecture employs Residual Atrous Spatial Pyramid Pooling (ResASPP) at the feature extractor to expand the receptive field, capture localized multiscale features, and enable deep feature learning with efficient aggregation. A two-stage U2-Net structure enhances hierarchical feature learning while maintaining a compact design, ensuring accurate context recovery. A dedicated refinement module, using two cascaded residual blocks (ResBlock), restores fine details to the occluded regions. Experimental results demonstrate its competitive performance, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 29.27 dB and Structural Similarity Index Measure (SSIM) of 0.875, which are two widely used metrics for evaluating reconstruction fidelity and perceptual quality, on both synthetic and real-world LF datasets, confirming its effectiveness in accurate occlusion removal. Full article
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