  Networks with large receptive field (RF) have shown advanced fitting ability
in recent years. In this work, we utilize the short-term residual learning
method to improve the performance and robustness of networks for image
denoising tasks. Here, we choose a multi-wavelet convolutional neural network
(MWCNN), one of the state-of-art networks with large RF, as the backbone, and
insert residual dense blocks (RDBs) in its each layer. We call this scheme
multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared
with other RDB-based networks, it can extract more features of the object from
adjacent layers, preserve the large RF, and boost the computing efficiency.
Meanwhile, this approach also provides a possibility of absorbing advantages of
multiple architectures in a single network without conflicts. The performance
of the proposed method has been demonstrated in extensive experiments with a
comparison with existing techniques.
