Application of Convolutional Neural Networks for Parallel Multi-Scale Feature Extraction in Noise Image Denoising

Although deep learning techniques have made significant advances in the field of images, existing methods still face challenges in processing complex, noisy images. In view of the limitation that most denoising models only focus on extracting single scale features, a new denoising network structure is proposed in this paper. Firstly, the channel attention mechanism and convolutional neural network are combined to construct a real image denoising model, and then the parallel multi-scale convolutional neural network is constructed by combining the adaptive dense connected residual block and parallel multi-scale feature extraction module. The results showed that the designed model can reach the stable state only after 121 and 86 iterations on the training set and the test set, and the denoising accuracy of the model is as high as 0.96. In addition, the research model has high computational efficiency and short denoising time when processing noisy images, and the processing time of an image is as low as 0.09s. Therefore, the proposed denoising structure has good denoising performance under different noise levels and types, and this study also provides a new idea for the application of deep learning in image denoising and other image processing tasks.