针对基于卷积神经网络图像降噪模型采用简单编码器-解码器结构而导致图像降噪性能差的问题，提出一种基于残差密集网络与注意力机制的残差密集图像降噪网络（residual dense image denoising network, RDIDNet）。首先，利用全局残差块增强网络模型的非线性映射能力；其次，引入双元素卷积注意力模块以实现RDIDNet模型解码过程中的自适应特征融合；最后，将RDIDNet降噪模型和14种代表性降噪方法进行对比，并进行消融实验，验证在基准模型上使用RDU Sub-Network、DE-CAM、PSNRLoss进行网络优化的有效性。实验结果表明，在Set12数据集、BSD68数据集中，RDIDNet在峰值信噪比（peak signal to noise ratio, PSNR）、结构相似性（structural similarity, SSIM）指标上相比传统经典方法BM3D分别平均提高1.03 dB和0.027 5；比基于Vision Transformers架构的SwinIR分别平均提高0.03 dB和0.001 4；比基于CNN的最新降噪方法NHNet分别平均提高0.22 dB和0.008 9。RDIDNet降噪网络更关注低频信息、模型训练更稳定，在有效消除图像噪声的同时能有效保留图像细节纹理，具有较好的表现。

In order to solve the problem of poor image denoising performance caused by the simple encoder-decoder structure of the convolutional neural network image denoising model, a residual dense image denoising network (RDIDNet) based on the residual dense network and attention mechanism was proposed. Firstly, the global residual block was used to enhance the nonlinear mapping ability of the network model. Secondly, the double-element convolutional attention module was introduced to realize the adaptive feature fusion in the decoding process of RDIDNet model. Finally, the RDIDNet denoising model was compared with 14 representative denoising methods, and ablation experiments were conducted to verify the effectiveness of using RDU Sub Network, DE-CAM, and PSNRLoss for network optimization on the benchmark model. The experimental results show that in the Set12 dataset and BSD68 dataset, RDIDNet improves the peak signal to noise ratio (PSNR) and structural similarity (SSIM) metrics by an average of 1.03 dB and 0.027 5, respectively, compared to the traditional classical method BM3D. Compared to SwinIR based on Vision Transformers architecture, the average improvement is 0.03 dB and 0.001 4, respectively. Compared to the latest CNN based denoising method NHNet, it has an average improvement of 0.22 dB and 0.008 9. The RDIDNet denoising network focuses more on low-frequency information and has more stable model training. It can effectively eliminate image noise while preserving image details and textures, and has good performance.