In this paper, we consider the problem of bearing estimation for spatially distributed sources in unknown spatially-correlated noise. Assumed that the noise covariance matrix is centro-Hermitian, a differential denoising scheme is developed. Combined it with the classic DSPE algorithm, a differential denoising estimator is formulated. Its modified version is also derived. Exactly, the differential processing is first imposed on the covariance matrix of array outputs. The resulting differential signal subspace (DSS) is then utilized to weight array outputs. The noise components orthogonal to DSS are eliminated. Based on eigenvalue decomposition of the covariance matrix of weighted array outputs, the DSPE null spectrum is constructed. The asymptotic performance of the proposed bearing estimator is evaluated in a closed form. Moreover, in order to improve the performance of bearing estimation in case of low signal-to-noise ratio, a modified differential denoising estimator is proposed. Simulation results show the effectiveness of the proposed estimators under the low SNR case. The impacts of angular spread and number of sensors are also investigated.