Research and design of image style transfer technology based on multi-scale convolutional neural network feature fusion

To reduce the occurrence of information loss and distortion in image style transfer, a method is proposed for researching and designing image style transfer technology based on multi-scale convolutional neural networks (CNNs) feature fusion. Initially, the VGG19 model is designed for coarse and fine-scale networks to achieve multi-scale CNN feature extraction of target image information. Subsequently, while setting the corresponding feature loss function, an additional least-squares penalty parameter is introduced to balance the optimal total loss function. Finally, leveraging the characteristics of stochastic gradient descent iteration, image features are fused and reconstructed to obtain better style transfer images. Experimental evaluations utilize peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), information entropy (IE), and mean squared error (MSE) as metrics for assessing the transferred images, comparing them with three typical image style transfer methods. Results demonstrate that the proposed method achieves optimal performance across all metrics, realizing superior image style transfer effects. This article conducts theoretical research on the research and design of image style transfer technology based on multi-scale convolutional neural network (CNN) feature fusion, and compares and analyzes the implementation effects of various experimental methods. Based on the original CNN image style transfer method, improvements and designs are made, and the image style transfer effect is well achieved. image