title: Novel example-based method for super-resolution and denoising of medical images.
abstract: In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.
authors.lastname: Dinh-Hoan Trinh
authors.forename: Marie
authors.initials: M
authors.lastname: Luong
authors.forename: Francoise
authors.initials: F
authors.lastname: Dibos
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Rocchisani
authors.lastname: Canh-Duong Pham
authors.forename: Truong Q
authors.initials: TQ
authors.lastname: Nguyen
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods*