{"title":"Efficient face image super-resolution with convenient alternating projection network","authors":"Xitong Chen, Yuntao Wu, Jiangchuan Chen, Jiaming Wang, Kangli Zeng","doi":"10.1049/sil2.12205","DOIUrl":null,"url":null,"abstract":"<p>The existing deep learning-based face super-resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super-resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super-resolution and ground-truth face images. Second, the self-correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state-of-the-art super-resolution methods.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12205","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12205","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The existing deep learning-based face super-resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super-resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super-resolution and ground-truth face images. Second, the self-correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state-of-the-art super-resolution methods.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf