Magnetic resonance imaging (MRI) has been widely applied in a number of clinical and preclinical applications. However, the resolution of the reconstructed images using conventional algorithms are often insufficient to distinguish diagnostically crucial information due to limited measurements. In this paper, we consider the problem of reconstructing a high resolution (HR) MRI signal from very limited measurements. The proposed algorithm is based on compressed sensing, which combines wavelet sparsity with the sparsity of image gradients, where the magnetic resonance (MR) images are generally sparse in wavelet and gradient domain. The main goal of the proposed algorithm is to reconstruct the HR MR image directly from a few measurements. Unlike the compressed sensing (CS) MRI reconstruction algorithms, the proposed algorithm uses multi measurements to reconstruct HR image. Also, unlike the resolution enhancement algorithms, the proposed algorithm perform resolution enhancement of MR image simultaneously with the reconstruction process from few measurements. The proposed algorithm is compared with three state-of-the-art CS-MRI reconstruction algorithms in sense of signal-tonoise ratio and full-with-half-maximum values.