Research on Image Up-scaling and Super-resolution Based on Convolutional Neural Network
DOI:
https://doi.org/10.54097/ef4k8k08Keywords:
Image Up-scaling, Image Super-resolution, Computer Vision, Convolutional Neural Network.Abstract
Image upsampling and super-resolution techniques are of great significance in many fields, which can improve image quality and enhance information extraction capabilities. Image upsampling and super-resolution techniques have wide applications in the real world, such as image quality enhancement, satellite and remote sensing images, and security and surveillance. This paper describes and analyzes a machine learning approach to up-scaling a low resolution images using Convolutional Neural Networks. We build on previous works on single image super-resolution. We train 3 structurally similar but different models and obtain an improvement of up to 3% compared to the more common image upscaling method, Bicubic Interpolation.
Downloads
References
Yang W, Zhang X, Tian Y, et al. Deep learning for single image super-resolution: A brief review[J]. IEEE Transactions on Multimedia, 2019, 21(12): 3106-3121.
Zhu X, Guo K, Fang H, et al. Cross view capture for stereo image super-resolution[J]. IEEE Transactions on Multimedia, 2021, 24: 3074-3086.
Lan R, Sun L, Liu Z, et al. MADNet: A fast and lightweight network for single-image super resolution[J]. IEEE transactions on cybernetics, 2020, 51(3): 1443-1453.
Shou Y, Meng T, Ai W, et al. Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis[J]. Neurocomputing, 2022, 501: 629-639.
Xue J, Zhao Y Q, Bu Y, et al. Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution[J]. IEEE Transactions on Image Processing, 2021, 30: 3084-3097.
Shou Y, Meng T, Ai W, et al. Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism[J]. Computational Intelligence and Neuroscience, 2022, 2022.
Shou Y, Cao X, Meng D. Masked Contrastive Graph Representation Learning for Age Estimation[J]. arXiv preprint arXiv:2306.17798, 2023.
Tian C, Xu Y, Zuo W, et al. Coarse-to-fine CNN for image super-resolution[J]. IEEE Transactions on Multimedia, 2020, 23: 1489-1502.
Ying R K, Shou Y, Liu C. Prediction Model of Dow Jones Index Based on LSTM-Adaboost[C]//2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2021: 808-812.
Zhu X, Guo K, Ren S, et al. Lightweight image super-resolution with expectation-maximization attention mechanism[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(3): 1273-1284.
Meng T, Shou Y, Ai W, et al. A Multi-Message Passing Framework Based on Heterogeneous Graphs in Conversational Emotion Recognition[J]. Available at SSRN 4353605, 2021.
Shou Y, Cao X, Meng D, et al. A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition[J]. arXiv preprint arXiv:2306.17799, 2023.
Dong W, Zhou C, Wu F, et al. Model-guided deep hyperspectral image super-resolution[J]. IEEE Transactions on Image Processing, 2021, 30: 5754-5768.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
