AI-Driven Image Processing: Theory, Methods, and Applications

Dear Colleagues,

This Special Issue aims to explore the transformative role of artificial intelligence (AI) in advancing image processing across theoretical, methodological, and applied domains. Rapid advancements in AI, particularly in deep learning, generative models, and computer vision, have revolutionized traditional image processing paradigms, enabling unprecedented accuracy, efficiency, and scalability. This issue seeks to showcase cutting-edge research addressing fundamental challenges—such as interpretability, robustness, and computational efficiency—while fostering innovative solutions for real-world applications.

The scope encompasses three core themes. Theory focuses on foundational AI frameworks, including novel architectures (e.g., CNNs, transformers, diffusion models), learning paradigms (self-supervised, few-shot learning), and theoretical insights into model generalization and adversarial robustness. Methods emphasize algorithmic innovations, such as lightweight models for edge computing, federated learning for privacy preservation, and multimodal fusion techniques. Submissions may also address dataset curation, ethical AI practices, and evaluation metrics tailored to diverse imaging contexts. Applications highlight AI-driven breakthroughs in domains like medical imaging (e.g., disease diagnosis, surgical planning), autonomous systems (object detection, scene understanding), environmental monitoring (satellite/remote sensing), and creative industries (image restoration, style transfer).

This Special Issue encourages interdisciplinary contributions bridging AI, computer vision, and domain-specific challenges. Researchers are invited to submit original articles, reviews, and case studies that advance the state of the art, address scalability and fairness concerns, or demonstrate transformative impacts. Research areas may include (but are not limited to) the following:

1. Image enhancement;
2. Image recovery;
3. Image super-resolution;
4. Image denoising;
5. Image deblurring;
6. Image fusion;
7. Image segmentation;
8. Image classification;
9. Object detection;
10. Computational imaging;
11. Polarization imaging;
12. Infrared imaging;
13. Hyperspectral imaging.

Dr. Junchao Zhang
Prof. Dr. Chuanli Wang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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.

• image processing
• image recovery
• image enhancement
• object detection
• image fusion
• computational imaging