FAST AND HIGH QUALITY LEARNING-BASED SUPER-RESOLUTION UTILIZING TV REGULARIZATION METHOD

Super-resolution image reconstruction is an important technology in many image processing areas such as image sensing, medical imaging, satellite imaging, and television signal conversion. It is also a key word 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 solutions. The problem of the learning-based method is its enormous computational time for image searching from the large database of training images. We have proposed a new Total Variation (TV) regularization super-resolution method that utilizes a learning-based super-resolution method. We have obtained excellent results in image quality improvement. However, our proposed method needs long computational time because of the learning-based method. In this paper, we examine two methods that reduce the computational time of the learning-based method. The resulting algorithms reduce complexity significantly while maintaining comparable image quality. This enables the adoption of learning-based super-resolution to the motion pictures such as HDTV and internet movies.