论文标题
多波力残留的残留密集卷积神经网络，用于图像deoing
Multi-wavelet residual dense convolutional neural network for image denoising
论文作者
论文摘要
近年来，具有大型接受场（RF）的网络显示出高级拟合能力。在这项工作中，我们利用短期残留学习方法来提高网络的性能和鲁棒性，以降低图像deno的任务。在这里，我们选择一个具有较大RF的最先进的网络之一，将多波力卷积神经网络（MWCNN）作为骨架，并在其每一层中插入残留的密集块（RDB）。我们称此方案多波动残留的密集卷积神经网络（MWRDCNN）。与其他基于RDB的网络相比，它可以从相邻层中提取对象的更多特征，保留大型RF并提高计算效率。同时，这种方法还提供了吸收单个网络中多个架构的优势，而没有冲突。在广泛的实验中，已证明了该方法的性能与现有技术的比较。
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.
