Deep Learning for Signal Processing Applications-2nd Edition

Dear Colleagues,

In recent times, deep learning has emerged as one of the most effective learning techniques in the broader area of artificial intelligence, especially for image and video analysis. Deep learning techniques have been used extensively for computer vision and recently for video analysis. In fact, in industry and academia, scientists and research scholars have come up with effective solutions for various image and video-related problems using different deep learning algorithms. The prime reason for the growing popularity of deep learning is that it can achieve higher recognition accuracy than earlier methods. With the applications of deep learning, excellent results have been achieved in image and video-related classification and segmentation. While substantial progress has been made in medical image analysis with deep learning, many issues still remain and new problems emerge such as deep learning in medical imaging focusing on MRI with high accuracy, the availability of limited datasets for classification tasks, major problems due to imbalanced datasets, detecting diseases from medical imaging, image registration, and computer-aided diagnosis. Apart from medical images, deep learning can also be applied to solve other problems such as image inpainting, sound classification, voice assistants and augmented intelligence, and high-resolution medical image reconstruction. Recently, due to the introduction of deep learning, video analysis has become more interesting. It used to be a challenging task as videos were data-intensive media with huge variations and complexities. Thanks to the deep learning technology, people working in multimedia are now able develop better performance-intensive techniques to analyse the content of the video.

This Special Issue aims to provide comprehensive coverage of cutting-edge research and state-of-the-art methods related to deep learning applications, especially with images and videos. Authors are requested to submit papers on topics including (but not limited to) the following:

• Image classification and segmentation using deep learning techniques.
• Object detection, image reconstruction, image super-resolution, and image synthesis using deep learning techniques.
• Cancer imaging using deep learning techniques.
• Deep learning in gastrointestinal endoscopy.
• Tumour detection using deep learning.
• Deep learning for image analysis using multimodality fusion.
• Image quality recognition methods inspired by deep learning.
• Advanced deep learning methods in computer vision with 3D data.
• Deep learning models to solve the task of MOT (Multiple Object Tracking).
• Novel applications of deep learning in a video classification framework.
• Deep learning techniques for video semantic segmentation.
• Applications of deep learning video and image forensics.
• Video summarization using deep learning.
• Human action recognition using deep learning.
• Application of deep learning in satellite imagery.
• Aerospace, defence, and communications.
• Industrial automation.
• Automotive.

Prof. Dr. Valentina E. Balas
Guest Editor

Manuscript Submission Information

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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. Applied Sciences 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.

• deep learning
• signal processing
• object detection
• image reconstruction

• Deep Learning for Signal Processing Applications in Applied Sciences (9 articles)