The neural activity signal of interest is often influenced by structural noise and random noise in functional Magnetic Resonance Imaging (fMRI) data. In order to eliminate noise effects in the analysis of activate voxels, the time series of voxels preprocessed by Statistical Parametric Mapping (SPM) were transformed by Activelets wavelet. After getting scale coefficient and detail coefficient, the two kinds of noise denoised were eliminated separately according to their corresponding characteristics. Firstly, the Independent Component Analysis (ICA) was used to identify and eliminate the structural noise sources. Secondly, an improved algorithm for spatial correlation was presented on the detail coefficient. In particular, in the improved algorithm, the voxel similarity in the neighborhood was used to determine whether the detail coefficient reflected the noise or the neural activity. Experimental results show that the processing of data effectively eliminate the effect of noise; specifically, the frame displacement decreased by 1.5mm and the percentage of spikes decreased by 2%; in addition, the false activation regions are obviously restrained in the spatial map got by denoised signals.