Abstract
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.
Keywords
- Computer Vision: CV: Machine learning for vision
- Computer Vision: CV: Representation learning
- Machine Learning: ML: Attention models
- Machine Learning: ML: Convolutional networks
