Journal of Imaging Science and Technology
Special Issue-Deep Learning Applications in Image Processing
Image processing tasks play an important role in the field of computer vision. With the increase of computing power and data resources, deep learning models are being actively explored in the imaging processing research community to learn abstract concepts and semantic information in images and to accomplish critical operational tasks, such as image classification and object detection, through large-scale training data and end-to-end learning methods. At the same time, deep learning has also led to remarkable progress in the fields of image super-resolution, image inpainting, and image generation to achieve higher quality and richer forms of expression to images. Image processing algorithms based on deep learning not only have accomplished major breakthroughs in academic research but also played an increasingly important role in industrial and practical applications, such as computer vision, autonomous driving, medical image analysis, security monitoring and other fields, bringing many conveniences and innovations to people's life and work.
Even with these notable advancements, image processing methods based on deep learning still face significant challenges and obstacles. First, deep learning models usually require a large amount of labeled data for training, which may be difficult for some specific fields or tasks. Second, the interpretability of deep learning models is poor. Deep learning models usually consist of a large number of parameters and complex structures, making the model's decision-making process difficult to understand and explain. In addition, deep learning models can be unstable for complex situations in images such as noise, occlusion, and deformation. This means that the performance of deep learning models can deteriorate and even produce erroneous yet unexplainable results when dealing with challenging image scenarios. Therefore, it is necessary to further solve the problems of insufficient data annotation, poorinterpretability, and model robustness to promote the development and application of image processing methods based on deep learning.
This special issue will focus on advancements in deep learning for the broad image processing applications including object recognition, image enhancement, visual understanding, etc. It also welcomes manuscripts on machine learning algorithm and system design for image generation, functional imaging, structural imaging, and other related topics on artificial intelligence to showcase the latest technological advancement. We encourage submission of original research in these fields as well as high-quality literature reviews providing new insights.
Topics for this special issue include but not limited to:
Deep learning applications in advanced image processing algorithms including object recognition, detection/classification, image generation/reconstruction, super-resolution, and denoising.
Innovative imaging methods that enhance structural imaging, functional imaging
Image enhancement and style transfer based on deep learning
Image semantic understanding and scene analysis via deep learning methodologies.
Case studies of deep learning-based image processing applications
Research on deep learning theories in the field of image processing
You will find submission details in theJIST Author Guidelines.
You must include a cover letter with the names of the authors and their
affiliations, addresses, faxes, and e‐mails. Prospective authors can
submit complete manuscripts electronically viahttps://jist.msubmit.net byDecember 8, 2023.
All submitted papers will be reviewed by at least two reviewers and
selected based on their originality, significance, relevance, and
clarity of presentation.
· JIST Submission portal open: July 10, 2023
· Submission deadline: December 8, 2023
· First round author notification: February 15, 2024
· First round revision submission: March 15, 2024
· Second round author notification: March 31, 2024
· Second round revision submission: April 10, 2024
· Final notification: April 15, 2024
· Publication (Tentative): May/June Issue in 2024
· 2023 4thInternational Symposium on Artificial Intelligence for Medical Science
· October 27 – 29, 2023 held in Chengdu, China
· Symposium Website:www.isaims.org