Image quality improvement for learning-based super-resolution with PCA
Shihou Miura, Yuichi Kawamoto, Satoshi Suzuki, Tomio Goto, Satoshi Hirano, Masaru Sakurai · 2012
Previously, we proposed a learning-based super-resolution method using the TV regularization method, which significantly reduced image processing time by removing database redundancy. However, there was a problem when noise appeared in reconstructed images because of an excessive reduction in database redundancy. In this paper, we propose a new learning-based super-resolution method, where noise is removed by utilizing Principal Components Analysis (PCA). The obtained algorithms significantly reduce the complexity and maintain a comparable image quality. This facilitates the adoption of learning-based super-resolution by motion pictures, e.g., Internet and HDTV movies.