A novel feature extraction model that obtains a complementary set of features across multiple spatial scales, while maintaining the original high-resolution features to preserve precise spatial details.

-   A regularly repeated mechanism for information exchange, where the features across multi-resolution branches are progressively fused together for improved representation learning.

-   A new approach to fuse multi-scale features using a selective kernel network that dynamically combines variable receptive fields and faithfully preserves the original feature information at each spatial resolution.

-   A recursive residual design that progressively breaks down the input signal in order to simplify the overall learning process, and allows the construction of very deep networks.

-   Comprehensive experiments are performed on five real image benchmark datasets for different image processing tasks including, image denoising, super-resolution and image enhancement. Our method achieves state-of-the-results on *all* five datasets. Furthermore, we extensively evaluate our approach on practical challenges, such as generalization ability across datasets.

Related Work
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With the rapidly growing image media content, there is a pressing need to develop effective image restoration and enhancement algorithms. In this paper, we propose a new approach capable of performing image denoising, super-resolution and image enhancement. Different from existing works for these problems, 