A novel framework for object removal from digital photograph

Jhing-Fa Wang, H. Hsu, Shang-Chia Liao
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引用次数: 8

Abstract

This work aims for a novel function for smart camera-redundant object removal from digital photograph. The proposed novel framework can fill the left lacuna region in the digital image. In previous related researches, texture synthesis and image inpainting construct the fundamentals of filling the lost image region. Texture synthesis can be used to fill the large hole of input texture, while image inpainting can be used to repair the small image gaps. In this paper, we propose an object removal framework by the sub-patch texture synthesis algorithm and weighted interpolation method with automatic repainting mechanism. In the filling process, the color distribution analysis is used to choose different methods. The exhaustive computation time is reduced by the weighted interpolation method. In order to repaint the faulty texture region intelligently, we use the color ratio gradients to detect the synthesized artifact region. The automatic artifact detection can lead repainting the faulty region without user intervention. The proposed algorithm can achieve better performance with seamless output images. The regular computation is also suitable for hardware architecture different from previous existing algorithms.
一种新的数字照片对象去除框架
本工作旨在为智能相机提供一种新的功能——从数码照片中去除冗余物体。该框架可以填补数字图像的左侧空白区域。在以往的相关研究中,纹理合成和图像补全是填补图像缺失区域的基础。纹理合成可以用来填补输入纹理的大洞,而图像补绘可以用来修复小的图像缺口。本文提出了一种基于子块纹理合成算法和带有自动重绘机制的加权插值方法的目标去除框架。在填充过程中,使用颜色分布分析来选择不同的方法。采用加权插值法减少了穷举计算时间。为了智能地重新绘制错误的纹理区域,我们使用颜色比例梯度来检测合成的伪影区域。自动伪影检测可以在没有用户干预的情况下重新绘制故障区域。该算法可以在无缝输出图像的情况下获得更好的性能。规则计算也适用于不同于现有算法的硬件架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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