Image Smoothing via Multiscale Global Perception

Image smoothing provides a fundamental operation for image processing, with a broad spectrum of applications. It is a challenging task which requires global analysis on image patterns with scale awareness. Existing deep models for image smoothing are insufficiently efficient in global perception and multi-scale processing. This letter proposes a deep model with an efficient multi-scale fusion architecture and a series of global processing blocks. The architecture enhances multi-scale feature flow by incorporating features of different scales into both the encoder and decoder blocks of a U-shape network, with multi-scale feature fusion modules inserted between the encoder and the decoder. The global processing blocks leverage the multi-axis processing mechanism to achieve joint local and global perception. Benefiting from these two key designs, our proposed model enjoys superiority in both smoothing performance and computational complexity, as demonstrated in the experiments on two benchmark datasets.