A fast image inpainting algorithm based on an adaptive scanning strategy

EAI Endorsed Trans. e Learn. Pub Date : 2023-08-15 DOI:10.4108/eetel.3141

H. .. R. .. Guo, W. .. H. .. Wang

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Abstract

OBJECTIVES: In exemplar-based image inpainting algorithms, there are often issues with the calculation of patch similarity for matching, suboptimal strategies for selecting matching patches, and low inpainting speed.METHODS: This paper first uses the variable scale cross-scan block line progressive scan to solve the problem of slow scanning speed and invalid priority formula. Then, an improved weight similarity formula is used for searching to solve the problem of poor computing strategy for similar matching patches. The search range of matching patches gradually increases from small to large until globally searching for similar matching patches to improve the efficiency of inpainting. To further improve the correctness of matching patch selection, this paper uses six levels of priority matching criteria for screening.RESULTS: The experimental results show that the inpainting effect of the proposed method is significantly improved in subjective vision, and the structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and inpainting speed of the inpainting results are all improved.CONCLUSION: For different types of images, the proposed method has a better inpainting effect and higher inpainting speed than the other three advanced methods.

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一种基于自适应扫描策略的快速图像绘制算法

目的:在基于样本的图像绘制算法中，经常存在匹配的补丁相似度计算，选择匹配补丁的次优策略以及低绘制速度等问题。方法:本文首先采用变尺度交叉扫描分块行逐行扫描，解决扫描速度慢、优先级公式无效的问题。然后，采用改进的权重相似度公式进行搜索，解决了相似匹配补丁计算策略差的问题;匹配块的搜索范围从小到大逐渐增大，直到全局搜索相似的匹配块，提高补漆效率。为了进一步提高匹配补丁选择的正确性，本文采用6级优先级匹配准则进行筛选。结果:实验结果表明，该方法在主观视觉下的补图效果明显提高，补图结果的结构相似度(SSIM)、峰值信噪比(PSNR)和补图速度均有提高。结论:对于不同类型的图像，所提出的方法比其他三种先进方法具有更好的涂抹效果和更快的涂抹速度。

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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EAI Endorsed Trans. e Learn.

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