Multi-wavelet residual dense convolutional neural network for image denoising
This work addresses image denoising, a common problem in computer vision, but it is incremental as it hybridizes existing architectures.
The authors tackled image denoising by combining a multi-wavelet CNN with residual dense blocks to enhance feature extraction and preserve a large receptive field, resulting in improved performance and efficiency compared to existing methods.
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.