<p>Image denoising aims to enhance image quality and visual effects, finding extensive applications in various fields such as digital photography, medical imaging, video processing, and image restoration. Although deep learning-based denoising methods have made significant progress, the post-denoising results often suffer from information loss and blurring, and the network structures tend to be complex. To address these challenges, this study proposes a Multi-Scale Connected Network for Image Denoising (MSCNet) based on U-net. Specifically, this paper introduces a skip connection designed to effectively fuse shallow fine-grained information and deep semantic information, thereby improving the effects of information transmission and integration. Additionally, a channel-wise attention mechanism and a non-linear module involving point-wise multiplication of half-channel activations are presented, aiming to extract more abundant semantic features while reducing network computation. Experimental results on multiple datasets validate that, compared to other denoising methods, MSCNet demonstrates superior denoising performance.</p>