基于深度学习的数字图像修复算法最新进展

Advances in Digital Image Inpainting Algorithms Based on Deep Learning

  • 摘要: 数字图像修复是一项利用计算机技术还原破损图像的缺失信息,从而实现自动修复破损图像的技术,其广泛应用于文物修复、图像去雾、电影特效生成等方面。近年来深度学习的发展为图像修复提供了新的思路,即将估计缺失信息的问题转为有条件的图像生成问题。基于深度学习的图像修复研究已成为底层计算机视觉问题的研究热点之一。本文对深度学习在数字图像修复领域的最新进展进行总结归纳,并详细阐述卷积模式和网络结构优化的研究进展,最后对未来的研究方向进行展望。

     

    Abstract: Digital image inpainting refers to the technology of restoring image missing information to automatically repair broken images, widely used in cultural relics restoration, image defogging, film special effects generation. In recent years, the missing information estimation problem of image inpainting can be regarded as a conditional image generation problem with deep learning. Many new ideas for image inpainting come forth. Image inpainting based on Deep learning has become one of the research hot topics in computer vision. This paper summarizes the latest developments of digital image inpainting based on deep learning, and introduces the progress in both convolution methods and network structures. Finally, future research directions are discussed.

     

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