Image detail enhancement of nanocomposites based on deep convolution neural networks

Abstract

Research on the image detail enhancement algorithm of nanocomposites can better improve the image quality of nanocomposites and facilitate researchers to understand the structural characteristics of materials. Due to the problems of low image quality and difficulty to retain the details of traditional nanocomposite image details enhancement algorithms, a new nanocomposite image details enhancement algorithm based on deep convolution neural networks (DCNN) is designed. Through histogram equalization, linear gray-scale stretching and median filtering, the image of nanocomposite is preprocessed, and then FCM algorithm is used for edge detection and image segmentation. Using color model transformation algorithm and DCNN, an image detail enhancement model including feature extraction and nonlinear mapping is constructed. Finally, the idea of image detail reconstruction is introduced, and image detail reconstruction is realized through a convolution layer. The results show that compared with the experimental comparison algorithm, the image processed by the proposed algorithm contains more detailed information and has higher image quality. The PSNR value reaches 27.1 dB, which indicates that the nanocomposite image has a better detail enhancement effect and can be widely used in the field of nanocomposite analysis and research.