A new ISR method based on the combination of modified K-SVD model and RAMP algrithm

Abstract

A new Image Super-resolution Reconstruction (ISR) method combined a modified K-means based Singular Value Decomposition (M_K-SVD) model and Regularized Adaptive Matching Pursuit (RAMP) algorithm is proposed in this paper. In the M_K-SVD model, the maximum sparsity of sparse coefficients is considered. In the condition of the unknown sparsity of the original signals, RAMP algorithm can choose automatically and adaptively the candidate set, and utilize the regularization process to implement the final support set so as to finish accurately the task of signal reconstruction. Combined the advantages of M_K-SVD and RAMP algorithm, for LR images and High Resolution (HR) images, the LR and HR dictionaries are trained. And then, utilized the optimized LR sparse coefficient vectors and the HR dictionary, the HR image patches can be estimated. And considered the original locations of HR image patches to be restored, the LR images can be reconstructed. However, LR images contain much unknown noise, so, before training dictionaries, the LR images are first preprocessed by M_K-SVD model. In test, human-made LR images (i.e. natural images׳ degraded versions) and real LR images (i.e. millimeter wave images, MMW) are respectively used to testify our method proposed. Further, compared our ISR method with those of the basic K-SVD, Regularized Orthogonal Matching Pursuit (ROMP), RAMP, and Sparsity Adaptive Matching Pursuit (SAMP) and so on, experimental results testified the ISR validity of our method proposed. Meanwhile, the Signal Noise Ratio (SNR) criterion is used to measure restored human-made LR images, and the Relative Signal Noise Ratio (RSNR) criterion is used to test the quality of MMW image restored. Experimental results prove that our method is indeed efficient in the research field of ISR reconstruction.