Applications of Convolutional Neural Networks in Imaging and Sensing

Dear Colleagues,

Convolutional Neural Networks (CNNs or ConvNets) are a class of deep neural networks that leverage spatial information, and are, therefore, well-suited to be applied to different tasks in image-processing and computer vision.

Exploiting deep and end-to-end learnable architectures, CNNs allow to learn the best features or abstract representations to solve the particular problem at hand. This flexibility to adapt to different problems is among the reasons they now represent the state-of-the-art in many challenging image-processing and computer vision applications, mostly outperforming traditional techniques based on handcrafted features.

Nonetheless, deep learning presents its own set of specific challenges: the need for large-cardinality training sets can make data labeling cumbersome and expensive, and the high computational complexity of neural models can be an obstacle to mobile embedding and user personalization.

This Special Issue covers all the topics related to the application of CNNs to image processing and computer vision tasks, as well as topics related to the definition of new CNN architectures, highlighting their advantages in addressing the problems currently faced by the imaging community.

Possible contributions to the Special Issue include, but are not limited to, the following topics:

• Image synthesis and rendering;
• Image restoration and enhancement;
• Color, multi-spectral, and hyper-spectral imaging;
• Image and video quality assessment;
• Texture, image and video analysis;
• Image and video recognition, classification, and retrieval;
• Biomedical and biological image processing and analysis;
• Image and video quality control and anomaly detection;
• Image processing for cultural heritage;
• Image and video dehazing;
• Image processing form material and object appearance, soft-metrology;
• Image processing applications;
• Image sensors.

Dr. Simone Bianco
Dr. Marco Buzzelli
Dr. Jean Baptiste Thomas
Guest Editors

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