  In image denoising networks, feature scaling is widely used to enlarge the
receptive field size and reduce computational costs. This practice, however,
also leads to the loss of high-frequency information and fails to consider
within-scale characteristics. Recently, dynamic convolution has exhibited
powerful capabilities in processing high-frequency information (e.g., edges,
corners, textures), but previous works lack sufficient spatial contextual
information in filter generation. To alleviate these issues, we propose to
employ dynamic convolution to improve the learning of high-frequency and
multi-scale features. Specifically, we design a spatially enhanced kernel
generation (SEKG) module to improve dynamic convolution, enabling the learning
of spatial context information with a very low computational complexity. Based
on the SEKG module, we propose a dynamic convolution block (DCB) and a
multi-scale dynamic convolution block (MDCB). The former enhances the
high-frequency information via dynamic convolution and preserves low-frequency
information via skip connections. The latter utilizes shared adaptive dynamic
kernels and the idea of dilated convolution to achieve efficient multi-scale
feature extraction. The proposed multi-dimension feature integration (MFI)
mechanism further fuses the multi-scale features, providing precise and
contextually enriched feature representations. Finally, we build an efficient
denoising network with the proposed DCB and MDCB, named ADFNet. It achieves
better performance with low computational complexity on real-world and
synthetic Gaussian noisy datasets. The source code is available at
https://github.com/it-hao/ADFNet.
