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Applied Sciences
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Computational Imaging: Algorithms, Technologies, and Applications
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Computational Imaging: Algorithms, Technologies, and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 573

Special Issue Editor

Prof. Dr. Hoon Yoo

E-Mail Website
Guest Editor
Department of Electronics Engineering, Sangmyung University, 20 Hongjimoon-2gil, Seoul 030031, Republic of Korea
Interests: image processing; 3D imaging; computational reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational imaging plays a critical role in advancing imaging technologies and their wide-ranging applications in fields such as medical imaging, 3D imaging, lensless imaging, synthetic aperture radar, seismic imaging, and ultrasound imaging. This Special Issue focuses on computational imaging algorithms and techniques aimed at addressing key challenges and improving image quality, speed, and overall functionality in various imaging systems.

The objective of this Special Issue is to engage the global image processing and signal sensing communities, providing a platform for researchers and engineers to present novel and original research in the field of computational sensing and imaging. Survey papers covering relevant advancements are also encouraged.

Prof. Dr. Hoon Yoo
Guest Editor

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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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • 3D integral imaging
  • depth estimation
  • image restoration and denoising
  • image enhancement
  • computational imaging for machine learning
  • deep learning for image reconstruction
  • underwater imaging and dehazing
  • data visualization

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Published Papers (1 paper)

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Research

21 pages, 49475 KB  
Article
NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression
by Ruoyu Yang, Huaixin Chen, Sijie Luo and Zhixi Wang
Appl. Sci. 2025, 15(15), 8686; https://doi.org/10.3390/app15158686 - 6 Aug 2025
Viewed by 253
Abstract
Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions [...] Read more.
Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods. Full article
(This article belongs to the Special Issue Computational Imaging: Algorithms, Technologies, and Applications)
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