In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections (MA-DFRNet), which achieves an ideal balance between denoising effect and network complexity. The paper presents a novel attention-based dual convolutional image denoising network (MA-DFRNet) that achieves an optimal trade-off between denoising performance and network complexity. MA-DFRNet comprises a multi-scale feature extraction network, dual convolutional neural networks, and a dynamic feature refinement attention mechanism. The multi-scale feature extraction network employs convolutions at various scales to enhance flexibility in capturing image features. The dual convolutional neural networks utilize skip connections and dilated convolutions in both upper and lower branches to expand the receptive field. Furthermore, the dynamic feature refinement attention mechanism enhances the accuracy and discriminability of feature representation. This structural design not only enlarges the receptive field but also effectively extracts and integrates image features, leading to significant improvements in denoising performance. The research findings demonstrate that the proposed MA-DFRNet outperforms state-of-the-art models in terms of PSNR and SSIM values across all levels of noise considered in the comparisons. The PSNR has increased by approximately 0.2 dB, while the SSIM has improved by around 1%. Notably, MA-DFRNet demonstrates greater robustness for images with higher noise levels and better preserves image details visually, effectively balancing denoising and detail retention.