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Applied Sciences
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Latest Research on Computer Vision and Image Processing, 2nd Edition
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Latest Research on Computer Vision and Image Processing, 2nd Edition

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 September 2026 | Viewed by 1835

Editors

Dr. Manuel Jesús Rodríguez Valido

E-Mail Website
Guest Editor
Department of Industrial Engineering, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: smart sensor networks; FPGA image processing; Internet of Things; autonomous driving; sustainable electric mobility
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gustavo Sutter Capristo

E-Mail Website
Guest Editor
Departamento de Tecnología Electrónica y de las Comunicaciones, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Interests: high-performance computing (HPC)
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Perez Nava

E-Mail Website
Guest Editor
Department of Computer Engineering and Systems, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: image and video processing; computer vision; virtual reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision is an artificial-intelligence discipline focused on instructing computers to comprehend and interpret visual data from the surrounding environment. By harnessing digital images captured by cameras and videos, in conjunction with deep-learning algorithms, computers can proficiently discern and categorize objects, subsequently enabling them to respond to visual stimuli effectively.

Currently, this discipline is supported by the concepts of image processing, feature detection and matching by pattern recognition and driven by artificial-intelligence technologies such as machine learning and deep learning, neural networks, image recognition and classification and object detection.

Challenges and limitations in computer vision are related to the following: data quality and quantity to increase accuracy; environmental sustainability, trade-off between increased computational requirements and energy efficiency; ethical and privacy concerns.

Research topics and application fields of interest for this Special Issue include, but are not limited to, the following:

  • Augmented reality
    • Transformatives sectors: manufacturing, retail, education and technological advances.
  • Vision models
    • Deep-learning techniques in computer vision.
    • Integration of vision and language in robotics.
  • Advanced satellite vision
    • Monitoring environmental and urban changes.
    • Disaster response and management.
    • Agricultural applications.
    • Climate change analysis.
  • Three-dimensional computer vision
    • Novel view rendering.
    • Generation of synthetic data for deep learning.
    • Enhancing autonomous vehicles and digital twin modeling.
  • Ethics in computer vision
    • Related to bias and privacy.
  • Edge computing
  • Computer vision in healthcare
    • Medical-image analysis.
    • Aiding surgeries.
    • Patient monitoring.
  • Synthetic data and generative AI
  • Real-time computer vision
    • Applications in enhancing security.
    • Crowd monitoring and management.
    • Industrial safety.

  • Deepfake detection

Dr. Manuel Jesús Rodríguez Valido
Prof. Dr. Gustavo Sutter Capristo
Dr. Fernando Perez Nava
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 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-anonymized 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.

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
  • image and video processing
  • image and video understanding
  • computer vision applications
  • machine learning
  • artificial intelligence
  • ethics
  • deepfakes
  • sustainability

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

Published Papers (3 papers)

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Research

23 pages, 16424 KB  
Article
Coordinating Drag-Based Structure Editing and Reference Style Transfer in Diffusion Models for Anime Images
by Youdong Ding, Wenjing Yu, Yafan Geng and Feifan Cai
Appl. Sci. 2026, 16(13), 6703; https://doi.org/10.3390/app16136703 - 4 Jul 2026
Viewed by 355
Abstract
Reference guided anime editing is challenging when the target requires both rendering style transfer and local structural change. Existing diffusion stylization methods that do not require training usually assume a fixed content layout, while drag-based editors deform local structures without enforcing a separate [...] Read more.
Reference guided anime editing is challenging when the target requires both rendering style transfer and local structural change. Existing diffusion stylization methods that do not require training usually assume a fixed content layout, while drag-based editors deform local structures without enforcing a separate style reference. Directly combining them is unstable: reference attention can disrupt handle tracking during dragging, whereas stylization after dragging can weaken the edited structure. This paper proposes AnchorHandoff, a temporally coordinated diffusion framework for joint drag and style editing. Drag optimization is performed with style injection disabled, followed by a short interval without style injection that lets the edited structure stabilize. A predicted clean sample from this state after dragging is then used as an anchor: content queries are refreshed from the anchor, and reference style keys and values are replayed on the edited layout. Soft correspondences from intermediate attention features guide style injection toward compatible regions without parsers or segmentation labels. On a curated anime benchmark, controlled comparisons, ablations, and a blind study with 36 participants show that AnchorHandoff reduces residual tracking error and feature structure distortion while maintaining comparable distribution level style alignment. The method remains limited under very large structural changes, but the results highlight temporal handoff as an important factor in joint anime structure and style editing. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing, 2nd Edition)
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33 pages, 11914 KB  
Article
WaveletMask: Wavelet-Domain Mask-Guided Degradation Detection for Old-Film Restoration
by Feifan Cai, Qi Zhang, Chang’an Xu and Youdong Ding
Appl. Sci. 2026, 16(13), 6415; https://doi.org/10.3390/app16136415 - 26 Jun 2026
Viewed by 273
Abstract
Old films suffer from scratches, dust, and brightness flicker caused by aging film stock and unstable analog exposure. Recurrent restoration frameworks suppress these artifacts under the guidance of degradation masks, yet pixel-domain frame differencing provides weak evidence for thin structural defects and confuses [...] Read more.
Old films suffer from scratches, dust, and brightness flicker caused by aging film stock and unstable analog exposure. Recurrent restoration frameworks suppress these artifacts under the guidance of degradation masks, yet pixel-domain frame differencing provides weak evidence for thin structural defects and confuses global brightness variation with content change. We present WaveletMask, a wavelet-domain degradation sensing framework that disentangles these two failure modes by construction: a high-frequency branch localizes transient structural defects from Haar detail-band differences between adjacent frames, a low-frequency branch isolates frame-level brightness deviations from coarse approximation responses, and a parameter-free maximum fusion rule passes the dominant cue to the recurrent gate. On the Synthetic and Real-World Old Video (SRWOV) benchmark, WaveletMask attains the best PSNR among ten re-trained methods (26.60 dB, +0.61 dB over the strongest competitor), and a paired comparison against the Recurrent Transformer Network (RTN) confirms a +0.45 dB gain while adding only 898 detector parameters. On real archival footage, WaveletMask removes scratches and flicker more cleanly while better preserving film texture and temporal stability. These results indicate that explicit wavelet-domain separation of structural and photometric cues offers a reliable, nearly cost-free upgrade for mask-guided recurrent restoration. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing, 2nd Edition)
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21 pages, 769 KB  
Article
Tabular-to-Image Encoding Methods for Melanoma Detection: A Proof-of-Concept
by Vanesa Gómez-Martínez, David Chushig-Muzo and Cristina Soguero-Ruiz
Appl. Sci. 2026, 16(5), 2459; https://doi.org/10.3390/app16052459 - 3 Mar 2026
Viewed by 694
Abstract
Deep learning (DL) models have demonstrated strong performance in dermatological applications, particularly when trained on dermoscopic images. In contrast, tabular clinical data—such as patient metadata and lesion-level descriptors—are difficult to integrate into DL-based pipelines due to their heterogeneous, non-spatial, and often low-dimensional nature. [...] Read more.
Deep learning (DL) models have demonstrated strong performance in dermatological applications, particularly when trained on dermoscopic images. In contrast, tabular clinical data—such as patient metadata and lesion-level descriptors—are difficult to integrate into DL-based pipelines due to their heterogeneous, non-spatial, and often low-dimensional nature. As a result, these data are commonly handled using separate classical machine learning (ML) models. In this work, we present a proof-of-concept study that investigates whether dermatological tabular data can be transformed into two-dimensional image representations to enable convolutional neural network (CNN)-based learning. To this end, we employ the Low Mixed-Image Generator for Tabular Data (LM-IGTD), a framework designed to transform low-dimensional and heterogeneous tabular data into two-dimensional image representations, through type-aware encoding and controlled feature augmentation. Using this approach, we encode low-dimensional clinical metadata, high-dimensional lesion-level statistical features extracted from dermoscopic images, as well as their feature-level fusion, into grayscale image representations. The resulting image representations serve as input to CNNs, and the performance is compared with ML models trained on tabular data. Experiments conducted on the Derm7pt and PH2 datasets show that traditional ML models generally achieve the highest Area Under the Curve values, while LM-IGTD-based representations provide comparable performance and enable the use of CNNs on tabular clinical data used in dermatology. Full article
(This article belongs to the Special Issue Latest Research on Computer Vision and Image Processing, 2nd Edition)
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