图像修复方法研究综述

Survey of Research on Image Inpainting Methods

图像修复是指恢复图像中受损区域像素,使其尽可能地与原始图像保持一致。图像修复不仅在计算机视觉任务中至关重要,同时也是其他图像处理任务研究的重要基石。然而现存图像修复相关总结研究较少,为了更好地学习和推进图像修复任务研究,对近十年的经典图像修复算法和极具代表性的深度学习图像修复方法进行了回顾和分析。首先,简单概述了经典的传统图像修复方法,并将其分为基于偏微分方程和基于样本的图像修复方法,同时进一步分析了传统图像方法局限性;着重分类且阐述了现有基于深度学习的图像修复方法,根据模型输出图像数量的不同,将其划分为单元图像修复和多元图像修复,结合方法应用图像、损失函数、类型、优势以及局限性对不同方法进行分析总结。之后,详述了图像修复方法常用数据集和定量评价指标,并给出图像修复方法在不同图像数据集上修复不同面积损坏区域的定量数据,根据定量数据对比分析了基于深度学习的图像修复方法性能。最后,归纳分析了现有图像修复方法的局限性,并对未来重点研究方向提出了新的思路和展望。

Image inpainting refers to restoring the pixels in damaged areas of an image to make them as consistent as possible with the original image.Image inpainting is not only crucial in the computer vision tasks,but also serves as an important cornerstone of other image processing tasks.However,there are few researches related to image inpainting.In order to better learn and promote the research of image inpainting tasks,the classic image inpainting algorithms and representative deep learning image inpainting methods in the past ten years are reviewed and ana-lyzed.Firstly,the classical traditional image inpainting methods are briefly summarized,and divided into partial dif-ferential equation-based and sample-based image inpainting methods,and the limitations of traditional image methods are further analyzed.Deep learning image inpainting methods are divided into single image inpainting and pluralistic image inpainting according to the number of output images of the model,and different methods are analyzed and summarized in combination with application images,loss functions,types,advantages,and limitations.After that,the commonly used datasets and quantitative evaluation indicators of image inpainting methods are described in detail,and the quantitative data of image inpainting methods to inpaint damaged areas of different areas on different image datasets are given.According to the quantitative data,the performance of image inpainting methods based on deep learning is compared and analyzed.Finally,the limitations of existing image inpainting methods are summarized and analyzed,and new ideas and prospects for future key research directions are proposed.