The current image denoising algorithms based on deep learning are unable to consider the local and global feature information comprehensively, which in turn affects the image denoising effect at the details. To address this problem, this study proposes a hybrid CNN and Transformer image denoising network (HCT-Net). First, CNN and Transformer coupling block (CTB) is proposed to construct a two-branch structure that integrates convolution and channel self-attention to alleviate the high computational overhead caused by relying solely on the Transformer. At the same time, the attention weights are dynamically allocated so that the network focuses on important feature information. Secondly, the self-attention enhanced convolution module (SAConv) is designed to adopt the progressive combination of modules and nonlinear transformations to attenuate the noise signal interference and identify local features under complex noise levels. Experimental results on six benchmark datasets show that HCT-Net has better feature perception ability than some current advanced denoising methods and can suppress high-frequency noise signals to recover the edge and detail information of images.