Principal topics include:

    Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality.
    Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing.
    Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing.
    Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video.
    Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography.
    Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security.

Special Issue on Deepfake Generation and Detection for Computer Vision Applications
Submission Date: 2024-10-01

Over the past few years, there has been remarkable progress in the field of deep learning techniques, leading to the emergence of a captivating and worrisome phenomenon known as deepfakes. Deepfakes are highly sophisticated computer-generated synthetic media that excel at manipulating and seamlessly blending visual content, resulting in the deceptive alteration of the human perceptual system. This technology has garnered significant attention and scrutiny due to its far-reaching implications across various domains. In the entertainment industry, deepfakes have generated both excitement and concerns, as they possess the potential to create hyper-realistic visual effects, revolutionizing filmmaking and virtual reality experiences. However, the malicious exploitation of deepfakes has also instigated apprehension within the realms of cybersecurity and privacy. These manipulated media can be used to fabricate persuasive misinformation, impersonate individuals, or manipulate sensitive visual data, raising serious ethical and security concerns. Recognizing the magnitude of this technological advancement, the primary objective of this special issue is to delve into the forefront of research and advancements in deepfake learning, with a specific focus on its applications within the domain of computer vision. By exploring innovative techniques, methodologies, and algorithms, we aim to enhance our understanding, detection, and mitigation of deepfake content, ultimately contributing to the development of robust countermeasures to combat this rapidly evolving challenge. In this special issue, we invite top-notch submissions pertaining to deepfake learning in the field of computer vision. Our primary objective is to enhance comprehension, detection, and countermeasures against deepfake content.

Please note that any extended conference papers should be expanded in accordance with our guidelines which can be found here: https://ietresearch.onlinelibrary.wiley.com/hub/expading-conference-papers.

Topics for this call for papers include but are not restricted to:

    Deepfake generation techniques using generative adversarial networks (GANs) and other deep learning architectures.
    Novel approaches for detecting and identifying deepfake content.
    Attribute-based analysis and recognition of deepfake images and videos.
    Deepfake localization and segmentation in images and videos.
    Deepfake forensics and countermeasures.
    Dataset creation and benchmarking for deepfake analysis.
    Ethical, legal, and social implications of deepfake technology.
    Applications of deepfake detection and prevention in real-world scenarios.
    Explainability and interpretability of deepfake models.
    Adversarial watermark for combating deepfake.

Guest Editors:

Jude Hemanth
Karunya Institute of Technology and Sciences,
India

Sur Singh Rawat
JSS Academy of Technical Education,
India

Tong Qiao
Hangzhou Dianzi University,
China

Special Issue on New Frontiers in Image and Video Processing for Sustainable Agriculture
Submission Date: 2024-10-01

The rapidly-evolving landscape of image processing, with the integration of cutting-edge technologies such as deep learning, has expanded its influence across various sectors. Agriculture, being a pillar of sustainable development, is on the cusp of a major technological transformation, necessitating the synergy of advanced sensors, image processing and machine learning.

Recognizing the symbiotic relationship between image processing advancements and the agricultural domain's intrinsic challenges, this special issue aims to bring to the fore the innovative applications of advanced image processing methodologies in agriculture to enable sustainable production. The focus is not only on addressing agricultural challenges but also on unravelling new research trajectories in image processing that could ripple into other sectors like remote sensing, robotics, and photogrammetry. Papers on pervasive computer vision and emerging paradigms of artificial intelligence, including federated learning and explainable artificial intelligence (XAI), are particularly welcome.

This special issue is aligned with the Sustainable Development Goals outlined in the 2030 agenda for sustainable development. Conversely, the agricultural domain provides a fertile ground for research challenges that motivate the exploration of new avenues.

Topics for this call for papers include but are not restricted to:

    Weed detection, classification and quantification
    Image-based plant phenotyping
    Plant and crop disease classification and grading
    Multispectral and hyperspectral imaging in sustainable agriculture
    Imaging from satellites and unmanned aerial vehicles
    Autonomous driving in agriculture
    Harvesting automation
    Robotics and robotic perception in agriculture
    Ethics and social impact of AI and image processing in agriculture
    Image-based crowd-sensing and participatory approaches in agriculture
    Explainable AI in precision agriculture
    Federated learning in precision agriculture
    Temporal analysis of agricultural fields using time-lapse imaging
    Multimodal data fusion: combining satellite, aerial, and ground-level imaging
    3D imaging and modeling for farm terrain analysis and planning

Guest Editors:

Davide Moroni
National Research Council of Italy (CNR),
Italy

Dimitrios Kosmopoulos
University of Patras,
Greece