In order to improve the efficiency of the eigenspace-based minimum variance (ESBMV) algorithm, a low-complexity minimum variance combined with eigenvalue decomposition (IBMV) algorithm is proposed. Firstly, the echo signals are transformed into beam domain by the discrete cosine transformation. Then, the eigenvalue decomposition of the sample covariance matrix is used to extract the signal subspace. The largest eigenvalue and its corresponding eigenvector can be extracted and other eigenvalues are taken the same value when the trace of sample covariance matrix remains unchanged. By these ways, the inverse operation of matrix can be simplified to the multiplication of vectors. In order to verify the validity of the proposed algorithm, Field II is introduced to test point targets and the cyst phantom. Experimental results indicate that the proposed IBMV with robustness to noises is obviously more efficient than ESBMV. Besides, the imaging quality of IBMV is better than delay and sum (DAS) method, minimum variance (MV) algorithm and beam domain minimum variance (BMV) method in terms of resolution and contrast ratio.