Image Enhancement with Deep Learning Techniques
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: closed (10 March 2022) | Viewed by 3105
Special Issue Editor
Interests: machine learning; computer vision; image understanding; medical image analysis and understanding; image super resolution; hyperspectral image super resolution and reconstruction; image draining
Special Issue Information
Dear Colleagues,
With the remarkable advancements of imaging technique, high-definition images in different fields can be easily captured for significantly enhancing the performance of different visual processing and perception systems. However, the imaging modalities captured under constrained conditions, such as medical fields, remote sensing, surveillance camera, and diversity environments, such as diverse weather, underwater, night, still have insufficient quality for providing acceptable performance in visual systems. Therein, image enhancement is an important low-level task for improving the visibility of the observed images and is generally used as an indispensable process in most downstream high-level visual applications for improving the generalization and wide applicability of real systems.
For the past few years, deep learning techniques have dominated the fields related to image processing and achieved significant success in terms of not only performance but also computational cost. With the amazing progression and benefits and of deep learning techniques, such as convolutional neural network, image enhancement for dealing with more challenge scenarios, such as heavy rain/haze, large magnification compress sensing, hyperspectral image reconstruction and so on, has attracted extensive attention, and deep research on these related fields would further contribute advancements in new science- and engineering-based technologies.
This Special Issue on image enhancement with deep learning techniques aims to invite researchers and professionals to contribute their original research papers that discuss ideas, theories, and methodologies along with practical examples, in implementing deep learning concepts in various image enhancement and its applications.
Dr. Xian-Hua Han
Guest Editor
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