Fine-grained image processing based on convolutional neural networks

Abstract

Abstract In the field of computer vision, convolutional neural networks are deep learning algorithms that can classify or detect images by learning image features. In order to achieve advanced recognition and analysis of images, multi-layer neural network models are employed in the discipline of image processing to gather and recall intricate aspects and patterns in the pictures. In this paper, we summarize and analyze the fine-grained image processing methods based on convolutional neural networks, including fine-grained image segmentation, image super-resolution reconstruction, and image edge detection methods. We also analyze the research progress of the three techniques both domestically and internationally. At the same time, experimental comparisons are conducted on mainstream datasets in the corresponding fields to obtain the performance of various fine-grained image processing methods. Finally, the development of convolutional neural networks in the field of fine-grained image processing is prospected.