DFMDA-Net: Dense Fusion and Multi-dimension Aggregation Network for Image Restoration

摘要

The U-shape (encoder-decoder) architecture, combined with effective blocks, has shown significant success in image restoration. In U-shape models, there is insufficient focus on the feature fusion problem between encoder and decoder features at the same level. Current methods often employ simplistic operations like summation or concatenation, which makes it difficult to strike a balance between performance and complexity. To address this issue, we propose a compression-in-the-middle mechanism, termed Integration-Compression-Integration (ICI), which effectively conducts dense fusion and avoids information loss. From the block design perspective, we design a multi-dimension aggregation (MDA) mechanism, capable of effectively aggregating features from both the channel and spatial dimension. Combining the IntegrationCompression-Integration feature fusion and the multi-dimension aggregation, our dense fusion and multi-dimension aggregation network (DFMDANet) achieves superior performance over state-ofthe-art algorithms on 16 benchmarking datasets for numerous image restoration tasks.