Regularized Orthogonal Match Pursuit(ROMP) is widely applied as a signal reconstruction algorithm. Despite its high efficiency, ROMP requires the prior knowledge of signal sparsity, and would be unstable if the sparsity level is improperly estimated. To overcome this drawback, a weak selection strategy was introduced to adaptively determine the number of atoms and the candidate atoms by estimating the relevance between iterative residue and measurement matrix of the original ROMP algorithm. Thus, an optimal atom set for the signal reconstruction procedure could be selected from the candidate atoms according to the regularization principle. Numerical results demonstrate that the proposed method outperforms other greedy algorithms with 0.5~1.5 dB higher PSNR and much lower MSE.