Recently, the deep learning (DL)-based solution for single image super-resolution reconstruction has been extensively used. Though these methods have shown exceptional results, the computational requirement is very high and requires high-end graphic processors. Motivated to establish an efficient method without such requirement, we propose a modified Markov random field (MRF) model and two-dimensional (2D) phase congruency-based single-image super-resolution reconstruction method for remote sensing images. The 2D phase congruency-based feature extraction method is used to compute features such as edge and texture maps to achieve higher accuracy in finding similar example patches in feature space. To address an essential aspect of successful texture reconstruction, we have incorporated a texture prior for the computation of joint probability in our work. Image Euclidean distance (Ieuc) is integrated to achieve higher accuracy in finding the similarity between image patches in feature space and modeling compatibility functions. The experimental results demonstrate that the results of the proposed method are at par with the DL-based methods and outperform other state-of-the-art methods in perceptual quality as well as peak signal-to-noise ratio and structural similarity index parameters. Moreover, it shows significant improvement in the texture regions while reconstructing sharper edges for ×4 upscaling.