Compressed-Sensing (CS) has been applied to ultrasound imaging to reduce data or to reduce the data acquisition time. There appears to be no report that uses CS framework to reduce the number of active receive elements in Conventional Focused Beamforming (CFB). Thus, in our previous work, a novel undersampling scheme based on Gaussian distribution was investigated and reported for reducing the number of active receive elements and data in CFB. In this paper, we exploit the Gaussian sampling based CS framework to improve the lateral resolution (LR) of the ultrasound system without increasing the system's complexity and cost. A notable difference from our previous work being the use of waveatom as the sparsifying basis, instead of 2D-Fourier basis, and analysis of the proposed framework for different receive aperture sizes. Simulation data for this study were generated using Field II, and experimental data were acquired from an in-vitro cyst phantom using Verasonics V-64 ultrasound scanner. The results indicate that the proposed framework of choosing a limited number of receive elements from a receive aperture length that is three or four times the corresponding active aperture size obtained from the same number of consecutive receive elements yields nearly twice an improvement in LR and about 27% increase in contrast to that of CFB reference image. Thus, the findings suggest a possibility to improve the LR of the current ultrasound system without increasing the system complexity, which will be significant for affordable point-of-care ultrasound systems. © 2019 IEEE.