title: Single image super-resolution using locally adaptive multiple linear regression.
abstract: This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.
authors.forename: Soohwan
authors.initials: S
authors.lastname: Yu
authors.forename: Wonseok
authors.initials: W
authors.lastname: Kang
authors.forename: Seungyong
authors.initials: S
authors.lastname: Ko
authors.forename: Joonki
authors.initials: J
authors.lastname: Paik