Basis pursuit denoising-based image superresolution using a redundant set of atoms

Digital investigations are very difficult to conduct from low-quality images generated by low-quality sensors. Therefore, we present a novel superresolution (SR) scheme that applies SR and denoising simultaneously, using the concept of sparse representation. For SR, a low-resolution (LR) input image is scaled up using our recently described adaptive interpolation scheme, and for each patch of the LR input, a vector of the sparse coefficients is then sought using a basis pursuit denoising sparse-coding algorithm instead of orthogonal matching pursuit. A high-resolution output is generated from the given LR input using the recovered vector of the sparse coefficients over a redundant set of atoms, i.e., an overcomplete dictionary. For the proposed technique, we modified the sparse-coding method of the K-SVD dictionary training approach by incorporating an efficient $$l_{1}$$l1-regularized least-squares method, i.e., a feature-sign search algorithm. Experimental evaluations validate the effectiveness of the proposed SR scheme.