Application and Improvement of Convolution Operation in Image Restoration

As a new concept and method proposed in the field of neural network, deep learning is introduced into machine learning, which promotes the rapid development of artificial intelligence and provides new ideas for image processing. One of the most representative algorithms is convolutional neural network. As the most important part of convolutional neural network, convolution can effectively extract the characteristic information of the input image and play an important role in image restoration. At present, many types of convolution methods have also been developed. In order to study the application of convolution in the image restoration and improvement, this paper introduces in detail the application of the convolution in the image processing and the detailed process of the calculation, and sums up the convolution loss of data information and the large amount of calculation, time-consuming long shortcomings, studied the development for the two problems of dilated convolution and grouping convolution and their improvement. The paper analyzes and summarizes the application trend of convolution in image processing. At the present stage, the operation speed of image processing determines the practicability of the algorithm. Larger calculation scale, richer data and higher intensity of calculation will become the focus of the development of convolution.