Structured artifacts are semi-regular, repetitive patterns that closely intertwine with genuine image content, making their removal highly challenging. In this paper, we introduce the Scale-Adaptive Deformable Transformer, an network architecture specifically designed to eliminate such artifacts from images. The proposed network features two key components: a scale-enhanced deformable convolution module for modeling scale-varying patterns with abundant orientations and potential distortions, and a scale-adaptive deformable attention mechanism for capturing long-range relationships among repetitive patterns with different sizes and non-uniform spatial distributions. Extensive experiments show that our network consistently outperforms state-of-the-art methods in diverse artifact removal tasks, including image deraining, image demoiréing, and image debanding.

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 paper 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.