An innovative reconstruction system using compressed sensing for nearfield electromagnetic imaging is presented in this paper. The proposed imaging system is tested and validated by creating a dictionary for head imaging of single and multiple brain tumor targets. The scattered time-domain signals are collected at few sensor positions, considering a limited number of possible spatial locations of tumor targets, and using spatial compressed sensing. The sensed signals are further preprocessed for spectral sparsity in frequency domain, resulting in further reduction in the number of samples. Simulation of the forward problem is presented, considering a head model, using CST Microwave Studio tool. Image reconstruction is performed considering various levels of signal to noise ratio. The quality of the reconstructed images of the target space reveals the potential of the developed imaging system.