1. Frequency-oriented hierarchical fusion network for single image raindrop removal.
- Author
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Wang, Juncheng, Zhang, Jie, Guo, Shuai, and Li, Bo
- Subjects
- *
RAINDROPS , *CONTEXTUAL learning , *MULTICASTING (Computer networks) , *KNOWLEDGE transfer , *PIXELS - Abstract
Single image raindrop removal aims at recovering high-resolution images from degraded ones. However, existing methods primarily employ pixel-level supervision between image pairs to learn spatial features, thus ignoring the more discriminative frequency information. This drawback results in the loss of high-frequency structures and the generation of diverse artifacts in the restored image. To ameliorate this deficiency, we propose a novel frequency-oriented Hierarchical Fusion Network (HFNet) for raindrop image restoration. Specifically, to compensate for spatial representation deficiencies, we design a dynamic adaptive frequency loss (DAFL), which allows the model to adaptively handle the high-frequency components that are difficult to recover. To handle spatially diverse raindrops, we propose a hierarchical fusion network to efficiently learn both contextual information and spatial features. Meanwhile, a calibrated attention mechanism is proposed to facilitate the transfer of valuable information. Comparative experiments with existing methods indicate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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