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Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement

In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase. Inspired by the dynamic deep networks that adapt the model structures or parameters conditioned on the inputs, we propose a DCP-guided hierarchical dynamic mechanism for image enhancement to adapt the model parameters and features from local to global as well as to keep spatial adjacency within the region. Specifically, channel-spatial-level, structure-level, and region-level dynamic components are sequentially applied. Channel-spatial-level dynamics obtain channel- and spatial-wise representation variations, and structure-level dynamics enable modeling geometric transformations and augment sampling locations for the varying local features to better describe the structures. In addition, a novel region-level dynamic is proposed to generate spatially continuous masks for dynamic features which capitalizes on the Dark Channel Priors (DCP). The proposed region-level dynamics benefit from exploiting the statistical differences between distorted and undistorted images. Moreover, the DCP-guided region generations are inherently spatial coherent which facilitates capturing local coherence of the images. The proposed method achieves state-of-the-art performance and generates visually pleasing images for multiple enhancement tasks,i.e. , image dehazing, image deraining and low-light image enhancement. The codes are available at https://github.com/DongLiangSXU/HDM.

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