title: Single image super-resolution with non-local means and steering kernel regression.
abstract: Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
authors: School of Electronic Engineering, Xidian University, Xi’an 710071, China. kbzhang0505@gmail.com
authors: Kaibing
authors: K
authors: Zhang
authors: Xinbo
authors: X
authors: Gao
authors: Dacheng
authors: D
authors: Tao
authors: Xuelong
authors: X
authors: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results