出版社：The Institute of Image Information and Television Engineers
摘要：Super-resolution image reconstruction is an important technology in many image processing areas, including image sensing, medical imaging, satellite imaging, and television signal conversion. It is also a key component of a recent consumer HDTV set that utilizes the CELL processor. Among various super-resolution methods, the learning-based method is one of the most promising. Its only problem is its enormous computational time for image searching from the large database of training images. We previously proposed a new total variation (TV) regularization super-resolution method that utilizes a learning-based super-resolution method and obtained excellent results in image quality improvement. However, this method requires a long computational time because of its use of the learning-based method. In the current study, we examine two methods of reducing this computational time. The proposed algorithms significantly reduce the complexity while maintaining a comparable image quality. This enables the application of learning-based super-resolution to motion pictures such as those on HDTV and Internet movies.
