This contribution proposes a convolutional neural network (CNN) filter for reference picture resampling (RPR) based super-resolution (SR) with wavelet decomposition. The proposed CNN filter takes the LR reconstructed frame (), LR prediction frame () and RPR upsampled frame () as the input for RPR-based SR. We adopt wavelet decomposition to make the same size as and as well as obtain the relationship between high frequency and low frequency components. Thus, the proposed CNN filter not only learns a mapping function between LR and HR images, but also effectively removes blocking artifacts in the reconstructed frame. Experimental results show that the proposed CNN filter in Y channel achieves -8.98% and -4.05% BD-rate reductions in AI and RA configurations over VTM-11.0_NNVC-2.0 anchor, respectively.