Call for Papers

The goal of image super-resolution (SR) is to restore a visually pleasinghigh-resolution (HR) image from a low-resolution (LR) input image or video sequence. HR imageshave higher pixel densities and finer details than LR images. Image SR has been proved to be of great significance in many applications, such as video surveillance, ultra-high definitionTV, low-resolution face recognition and remote sensing imaging. Benefiting from its broad application prospects, SRhas attracted huge interest, and currently is oneof the most active research topics in image processing and computer vision.

Recently, deep learning has become one of the core technologies of computer vision and artificial intelligence. Deep learning is a data-driven technology and its performance heavily relies on large-scale labeled data, e.g., ImageNet and MS COCO. Unfortunately, it is rather expensive to collect and annotate large-scale image data from the real world, the collected real images are limited in covering complex environmental conditions, and the real scenes are uncontrollable and unrepeatable.

During the past decade, large-scale multimedia data (e.g., video, images, audios, text) can be easily collected in different fields and pattern discovery from these raw data has been attracting increasing interests in the multimedia domain. Semantically understanding multimedia data can substantially enhance their practical applications.

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes.

Artificial intelligence (AI) is a comprehensive area of study consisting of numerous subjects including intelligent search, machine learning, knowledge management, pattern recognition, uncertain management, neural networks, and so forth. With the development of big data and deep learning, AI has become a subject of board and current interest; recent key breakthroughs in information technology especially in computation ability are often related to AI, and becomes a key factor to advance the development of AI. Traditional AI technologies have challenges in processing massive data, large-scale communication as well as collaboration, and collaborative computing of various algorithms. To meet these challenges, parallel computing has been introduced.




