DMET: Dynamic Mask-Enhanced Transformer for Generalizable Deep Image Denoising

• Published in: Mathematics (Basel) Vol. 13; no. 13; p. 2167
• Main Authors: Zhu, Tong, Li, Anqi, Wang, Yuan-Gen, Su, Wenkang, Jiang, Donghua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Attention; channel attention; Computational linguistics; Computer vision; Datasets; Deep learning; Electric transformers; Image acquisition; image denoising; Language processing; Machine vision; masked training; Natural language interfaces; Neural networks; Noise; Noise reduction; Regularization methods; Semantics
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math13132167

Different types of noise are inevitably introduced by devices during image acquisition and transmission processes. Therefore, image denoising remains a crucial challenge in computer vision. Deep learning, especially recent Transformer-based architectures, has demonstrated remarkable performance for image denoising tasks. However, due to its data-driven nature, deep learning can easily overfit the training data, leading to a lack of generalization ability. In order to address this issue, we present a novel Dynamic Mask-Enhanced Transformer (DMET) to improve the generalization capacity of denoising networks. Specifically, a texture-guided adaptive masking mechanism is introduced to simulate possible noise in practical applications. Then, we apply a masked hierarchical attention block to mitigate information loss and leverage global statistics, which combines shifted window multi-head self-attention with channel attention. Additionally, an attention mask is applied during training to reduce discrepancies between training and testing. Extensive experiments demonstrate that our approach achieves better generalization performance than state-of-the-art deep learning models and can be directly applied to real-world scenarios.