Research on optimization and application of image processing algorithm based on deep learning

Firstly, this paper discusses the key technologies of deep learning in image processing, including network structure optimization (ResNet, DenseNet and MobileNet), loss function design, data enhancement and preprocessing methods, and optimization algorithms and skills. Through experiments, the models of ResNet-34, Mobilene ETV 3 and DenseNet-121 are used to perform image classification, super-resolution reconstruction and lightweight classification on datasets such as CIFAR-10, DIV2K and ImageNet-1K. The results show that ResNet-34 has the highest accuracy (94.2%) in the classification task, but Mobilene ETV 3 has more advantages in parameter quantity and reasoning speed. In the super-resolution task, the PSNR of L1 loss is 1.2 dB higher than that of MSE loss, which shows a better edge reconstruction effect. In addition, the experiment also verified the effectiveness of the strategy of dynamically adjusting learning rate and network pruning. Although the performance of the model is limited when the amount of data is insufficient, and the calculation cost of the deep network is high, by incorporating an automated data augmentation strategy and model compression techniques, the model's generalization capability and operational efficiency are anticipated to be further enhanced.