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    亓文法, 刘宇鑫, 郭宗明. 尔纹图案自动去除技术综述[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220797
    引用本文: 亓文法, 刘宇鑫, 郭宗明. 尔纹图案自动去除技术综述[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220797
    Qi Wenfa, Liu Yuxin, Guo Zongming. Survey of Automatic Removal of Moiré Pattern[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220797
    Citation: Qi Wenfa, Liu Yuxin, Guo Zongming. Survey of Automatic Removal of Moiré Pattern[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220797

    尔纹图案自动去除技术综述

    Survey of Automatic Removal of Moiré Pattern

    • 摘要: 如今,数码相机和智能手机在人们的生活中扮演着越来越重要的角色,已经成为人们感知世界、记录信息和沟通交流的主要工具. 当使用这些设备拍摄电子屏幕时,显示设备和摄像头传感器网格之间往往会发生混叠,通常导致图片中存在不规则分布的摩尔纹干扰图案,从而严重影响了拍摄图像的视觉质量效果. 因此,摩尔纹图案去除方法研究对于拍摄图像的后期处理具有重要意义. 为此,详细梳理了摩尔纹去除研究的发展脉络,并根据不同的适用场景和技术实现将现有方法分为2类:基于先验知识的摩尔纹去除方法和基于深度学习的摩尔纹去除方法. 鉴于深度学习网络中训练数据集的收集和对齐方式不同,该类方法又分为基于卷积神经网络(CNN)和基于生成式对抗网络(GAN)的摩尔纹去除方法. 在此基础上,选择相同的公开数据集,对主流的深度学习方法进行算法实现和性能对比分析,并分别总结了各类方法的优缺点. 最后,对未来的研究方向进行展望.

       

      Abstract: Nowadays, digital cameras and smart phones play an increasingly important role in daily lives, and have become the main tools for people to perceive the world, record information and communicate with each other. When these devices are used to shoot electronic screens, the irregular Moiré patterns in the image are produced due to the overlap of the display devices and digital grids of the camera sensor, which seriously affects the visual quality of the captured images. Therefore, the removal of Moiré pattern is of great significance for the post-processing of captured images. In this paper, recent research about Moiré removal is reviewed in detail, and the existing methods are classified into two categories according to different application scenarios and technical implementations: prior knowledge based Moiré removal and deep learning based methods respectively. According to different training data acquisition and alignment approaches, deep learning based Moiré removal techniques can be divided into convolutional neural network (CNN) based methods and generative adversarial network (GAN) based methods. Then, based on the same public dataset, we implement the mainstream deep learning based Moiré removal algorithms, compare and analyze their performance, and summarize the advantages and disadvantages of various methods. Finally, discuss possible directions for future research.

       

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