New Challenges in Image Processing and Pattern Recognition

Topic Information

Dear Colleagues,

The topic “New Challenges in Image Processing and Pattern Recognition” aims to provide a comprehensive and forward-looking platform for the exchange of innovative research addressing emerging issues, novel methodologies, and multidisciplinary applications in the fields of image processing and pattern recognition. As digital imagery becomes increasingly central across domains—ranging from autonomous systems and biomedical imaging to security, remote sensing, and industrial automation—new challenges continue to arise due to growing data complexity, real-time demands, and the integration of artificial intelligence.

This topic seeks contributions that push the boundaries of current techniques or offer novel perspectives on traditional problems. We welcome both theoretical developments and practical applications that demonstrate clear innovation and impact.

The scope of the topic includes, but is not limited to:

• Advanced image enhancement, restoration, and reconstruction techniques;
• Deep learning architectures and transformer models for image understanding;
• Explainable AI (XAI) and trustworthy pattern recognition systems;
• Real-time and embedded vision systems for edge computing;
• Multimodal data fusion (e.g., RGB-D, thermal, hyperspectral);
• Three-dimensional vision, shape analysis, and object recognition in complex scenes;
• Bio-inspired and physics-informed image analysis models;
• Adversarial attacks, robustness, and secure pattern recognition;
• Image processing in biomedical, environmental, industrial, and artistic domains;
• Benchmarking and evaluation metrics for image processing algorithms.

The topic encourages interdisciplinary approaches and collaborative studies that link computer vision with neuroscience, cognitive science, computational imaging, and other related fields.

Dr. Pei-Ju Chiang
Dr. Cheng-Lun Chen
Dr. Ping-Huan Kuo
Prof. Dr. Wen-Yang Chang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Journal of Imaging jimaging	3.3	6.7	2015	18 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	6.0	9.9	2019	27 Days	CHF 1800	Submit
Modelling modelling	1.5	2.2	2020	24.9 Days	CHF 1200	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Computers Electronics J. Imaging MAKE Modelling

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

17 pages, 8549 KB  

Open AccessArticle

Print Quality Assessment of QR Code Elements Achieved by the Digital Thermal Transfer Process

by Igor Majnarić, Marija Jelkić, Marko Morić and Krunoslav Hajdek

J. Imaging 2026, 12(2), 86; https://doi.org/10.3390/jimaging12020086 - 18 Feb 2026

Viewed by 151

Abstract

The new European Regulation (EU) 2025/40 includes provisions on modern packaging and packaging waste. It defines the use of image QR codes on packaging (items 71 and 161) and in personal documents, making line barcodes a thing of the past. The definition of [...] Read more.

The new European Regulation (EU) 2025/40 includes provisions on modern packaging and packaging waste. It defines the use of image QR codes on packaging (items 71 and 161) and in personal documents, making line barcodes a thing of the past. The definition of a QR code is precisely specified in ISO/IEC 18004:2024. However, their implementation in printing systems is not specified and remains an important factor for their future application. Digital foil printing is a completely new hybrid printing process for applying information to highly precise applications such as QR codes, security printing, and packaging printing. The technique is characterized by a combination of two printing techniques: drop-on-demand UV inkjet followed by thermal transfer of black foil. Using a matte-coated printing substrate (Garda Matt, 300 g/m²), Konica Minolta KM1024 LHE Inkjet head settings, and a transfer temperature of 100 °C, the size of the square printing elements in QR codes plays a decisive role in the quality of the decoded information. The aim of this work is to investigate the possibility of realizing the basic elements of the QR code image (the profile of square elements and the success of realizing a precisely defined surface) with a variation in the thickness of the UV varnish coating (7, 14 and 21 µm), realized using the MGI JETvarnish 3DS digital machine. The most commonly used rectangular elements with a surface area of 0.01 cm² were tested: 0.06 cm², 0.25 cm², 1 cm², 4 cm², and 16 cm². The results showed that the imprint quality is uneven for the smallest elements (square elements with base lengths of 0.1 cm and 0.25 cm). The effect is especially visible with a minimum UV varnish application of 7 μm (1 drop). By increasing the amount of UV varnish and the application thickness to 14 μm (2 drops) and 21 μm (3 drops), respectively, a significantly more stable, even reproduction of the achromatic image is achieved. The highest technical precision was achieved with a UV varnish thickness of 21 μm. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

►▼ Show Figures

Figure 1

31 pages, 4972 KB  

Open AccessArticle

Minutiae-Free Fingerprint Recognition via Vision Transformers: An Explainable Approach

by Bilgehan Arslan

Appl. Sci. 2026, 16(2), 1009; https://doi.org/10.3390/app16021009 - 19 Jan 2026

Viewed by 397

Abstract

Fingerprint recognition systems have relied on fragile workflows based on minutiae extraction, which suffer from significant performance losses under real-world conditions such as sensor diversity and low image quality. This study introduces a fully minutiae-free fingerprint recognition framework based on self-supervised Vision Transformers. [...] Read more.

Fingerprint recognition systems have relied on fragile workflows based on minutiae extraction, which suffer from significant performance losses under real-world conditions such as sensor diversity and low image quality. This study introduces a fully minutiae-free fingerprint recognition framework based on self-supervised Vision Transformers. A systematic evaluation of multiple DINOv2 model variants is conducted, and the proposed system ultimately adopts the DINOv2-Base Vision Transformer as the primary configuration, as it offers the best generalization performance trade-off under conditions of limited fingerprint data. Larger variants are additionally analyzed to assess scalability and capacity limits. The DINOv2 pretrained network is fine-tuned using self-supervised domain adaptation on 64,801 fingerprint images, eliminating all classical enhancement, binarization, and minutiae extraction steps. Unlike the single-sensor protocols commonly used in the literature, the proposed approach is extensively evaluated in a heterogeneous testbed with a wide range of sensors, qualities, and acquisition methods, including 1631 unique fingers from 12 datasets. The achieved EER of 5.56% under these challenging conditions demonstrates clear cross-sensor superiority over traditional systems such as VeriFinger (26.90%) and SourceAFIS (41.95%) on the same testbed. A systematic comparison of different model capacities shows that moderate-scale ViT models provide optimal generalization under limited-data conditions. Explainability analyses indicate that the attention maps of the model trained without any minutiae information exhibit meaningful overlap with classical structural regions (IoU = 0.41 ± 0.07). Openly sharing the full implementation and evaluation infrastructure makes the study reproducible and provides a standardized benchmark for future research. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

►▼ Show Figures

Figure 1

12 pages, 13049 KB  

Open AccessArticle

A Hybrid Vision Transformer-BiRNN Architecture for Direct k-Space to Image Reconstruction in Accelerated MRI

by Changheun Oh

J. Imaging 2026, 12(1), 11; https://doi.org/10.3390/jimaging12010011 - 26 Dec 2025

Viewed by 310

Abstract

Long scan times remain a fundamental challenge in Magnetic Resonance Imaging (MRI). Accelerated MRI, which undersamples k-space, requires robust reconstruction methods to solve the ill-posed inverse problem. Recent methods have shown promise by processing image-domain features to capture global spatial context. However, these [...] Read more.

Long scan times remain a fundamental challenge in Magnetic Resonance Imaging (MRI). Accelerated MRI, which undersamples k-space, requires robust reconstruction methods to solve the ill-posed inverse problem. Recent methods have shown promise by processing image-domain features to capture global spatial context. However, these approaches are often limited, as they fail to fully leverage the unique, sequential characteristics of the k-space data themselves, which are critical for disentangling aliasing artifacts. This study introduces a novel, hybrid, dual-domain deep learning architecture that combines a ViT-based autoencoder with Bidirectional Recurrent Neural Networks (BiRNNs). The proposed architecture is designed to synergistically process information from both domains: it uses the ViT to learn features from image patches and the BiRNNs to model sequential dependencies directly from k-space data. We conducted a comprehensive comparative analysis against a standard ViT with only an MLP head (Model 1), a ViT autoencoder operating solely in the image domain (Model 2), and a competitive UNet baseline. Evaluations were performed on retrospectively undersampled neuro-MRI data using R = 4 and R = 8 acceleration factors with both regular and random sampling patterns. The proposed architecture demonstrated superior performance and robustness, significantly outperforming all other models in challenging high-acceleration and random-sampling scenarios. The results confirm that integrating sequential k-space processing via BiRNNs is critical for superior artifact suppression, offering a robust solution for accelerated MRI. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

►▼ Show Figures

Figure 1

17 pages, 3889 KB  

Open AccessArticle

STGAN: A Fusion of Infrared and Visible Images

by Liuhui Gong, Yueping Han and Ruihong Li

Electronics 2025, 14(21), 4219; https://doi.org/10.3390/electronics14214219 - 29 Oct 2025

Viewed by 724

Abstract

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative [...] Read more.

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article

(This article belongs to the Topic New Challenges in Image Processing and Pattern Recognition)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-4