基于局部和全局特征解耦的图像去噪网络

Image denoising network based on local and global feature decoupling

针对当前基于Transformer的图像去噪算法侧重于捕获图像的全局特征,而忽视局部特征对于恢复图像细节关键作用的问题,提出一种基于局部和全局特征解耦的图像去噪网络。该网络包含2个基于混合Transformer模块(HTB)的多尺度分支和1个基于卷积神经网络(CNN)的单尺度分支,旨在将HTB强大的全局建模能力与CNN的局部建模优势有机结合,生成上下文信息丰富且空间细节准确的输出。HTB采用自注意力机制自适应地对空间和通道维度的依赖关系建模,以激活范围更广的输入像素进行重建。鉴于不同分支间可能存在的信息冲突,设计特征传递模块,通过跨分支传递全局特征并抑制低频信息,从而确保各分支间的协同作用。实验结果表明,在真实世界图像数据集SIDD上,与基于Transformer的去噪网络Uformer相比,所提网络的峰值信噪比(PSNR)提高了0.09 dB,结构相似度(SSIM)提高了0.001;在合成图像数据集Urban100上,与多阶段去噪网络MSPNet(Multi-Stage Progressive denoising Network)相比,所提网络的平均PSNR提高了0.41 dB。可见,所提网络能有效去除图像噪声,并重建出更精细的纹理细节。

Concerning the problem that current Transformer-based algorithms focus on capturing the global features of images,but ignore the key role of local features to restore image details,an image denoising network based on local and global feature decoupling was proposed.The proposed network included two multi-scale branches based on Hybrid Transformer Block(HTB)and a single-scale branch based on Convolutional Neural Network(CNN),aiming at combining powerful global modeling capability of HTB with local modeling advantage of HTB,and yielding outputs with enriched contextual information and precise spatial details.Within the HTB,self-attention mechanism was employed to adaptively model spatial-and channel-dimensional dependencies,activating a wider range of input pixels for reconstruction.Given the potential information conflicts across different branches,feature transfer block was designed to facilitate cross-branch propagation of global features and suppress low-frequency information,thereby ensuring collaborative interactions among the branches.Experimental results showed that:on the real-world image dataset SIDD,compared with Transformer-based denoising network Uformer,the proposed network improved Peak Signal-to-Noise Ratio(PSNR)by 0.09 dB and Structural SIMilarity(SSIM)by 0.001;on the synthetic image dataset Urban100,compared with multi-stage denoising network MSPNet(Multi-Stage Progressive denoising Network),the average PSNR of the proposed network was improved by 0.41 dB.It can be seen that the proposed network effectively removes image noise and reconstructs finer texture details.