摘要
图像自身信息对图像重建具有天然鲁棒性,但多数超分辨率方法并未充分利用全局特征信息。文中提出了一种新图像超分辨率模型,混合了多种及多尺度注意力,包括多尺度混合非局部注意力上采样模块和残差密集注意力模块两个新的模块。与以往非局部方法不同,多尺度混合非局部注意力上采样模块混合了基于像素和基于块的非局部注意力,并在多个尺度上建立块级别的上采样映射关系,使全局搜索空间更广。残差密集注意力模块建立通道和空间维度的注意力关联,通过密集连接增强了前后注意力信息的传递和融合。在多个基准数据集上进行了定量和定性评估,实验结果表明,该模型在性能和重建质量方面优于同类超分辨率模型。
Image itself information is naturally robust to image reconstruction,yet most current super-resolution methods do not fully utilize global feature information.This study proposes a new image super-resolution model mixing multiple and multi-scale attentions,including two new modules:Multi-scale hybrid non-local attention upsampling module and residual dense attention block.Different from previous nonlocal methods,multi-scale hybrid non-local attention upsampling module mixes pixel-based and patch-based nonlocal attention and establishes patch-level upsampling mapping relationships at multiple scales,which enables a wider global search space.The residual dense attention block establishes attention associations in channel and spatial dimensions,which enhances the transfer and fusion of front-to-back attention information through dense connections.In this study,quantitative and qualitative evaluations are conducted on several benchmark datasets,and the experimental results show that the model outperforms similar super-resolution models in terms of performance and reconstruction quality.
作者
蒯新晨
李烨
KUAI Xinchen;LI Ye(School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《电子科技》
2024年第9期34-42,共9页
Electronic Science and Technology
基金
人工智能四川省重点实验室项目(2022RZY02)。
关键词
图像超分辨率
多尺度
注意力机制
非局部
循环网络
密集连接
上采样
自相似性
image super-resolution
multi-scale
attention mechanism
non-local
recurrent network
dense connection
upsampling
self-similarity
