Dense Hybrid Attention Network for Palmprint Image Super-Resolution
提出一种密集混合注意力网络，通过并行CNN和Transformer分支学习掌纹局部与全局特征，并设计增强的空间和通道注意力模块，以恢复低质量掌纹图像的清晰纹理和边缘，提升识别可靠性。
Palmprint has attracted increasing attention for biometric recognition in recent years due to its outstanding reliability, user-friendliness and hygiene. However, existing palmprint recognition methods usually require high-quality palmprint images with clear texture and line patterns; however, in practical applications palmprint images are usually of low quality. In this study, we propose a dense hybrid attention (DHA) network for palmprint image super-resolution (SR) by recovering the clear palmprint-specific characteristics. The proposed DHA network first obtains the high-dimensional shallow representation via a single convolution layer, and then jointly learns the local and global palmprint-specific features via parallel convolutional neural network (CNN)-and transformer-based branches. Particularly, we develop two enhanced spatial and channel attention (CA) modules to adaptively emphasize the local position-specific characteristics of palmprints, such that the SR palmprint images can be well recovered with clear texture and edge characteristics. Experimental results on three publicly used palmprint databases clearly show the effectiveness of the proposed method for palmprint image SR.