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.