Joint multi-dimensional dynamic attention and transformer for general image restoration

Understand

Outdoor images often suffer from severe degradation due to rain, haze, and noise, impairing image quality and challenging high-level tasks.

• Current image restoration methods struggle to handle complex degradation while maintaining efficiency.
• This paper introduces a novel image restoration architecture that combines multi-dimensional dynamic attention and self-attention within a U-Net framework.
• To leverage the global modeling capabilities of transformers and the local modeling capabilities of convolutions, we integrate sole CNNs in the encoder-decoder and sole transformers in the latent layer.