In this article, a blockwise regression-based image inpainting framework is proposed. The core idea is to fill the unknown region in two stages: Extrapolate the edges to the unknown region and then fill the unknown pixels values in each sub-region demarcated by the extended edges. Canny edge detection and linear edge extension are used to respectively identify and extend edges to the unknown region followed by regression within each sub-region to predict the unknown pixel values. Two different regression models based on K-nearest neighbours and support vectors machine are used to predict the unknown pixel values. The proposed framework has the advantage of inpainting without requiring prior training on any image dataset. The extensive experiments on different images with contrasting distortions demonstrate the robustness of the proposed framework and a detailed comparative analysis shows that the proposed technique outperforms existing state-of-the-art image inpainting methods. Finally, the proposed techniques are applied to MRI images suffering from susceptibility artifacts to illustrate the practical usage of the proposed work.

中文翻译：

---

一种使用回归的新型图像修复框架

在本文中，提出了一种基于块回归的图像修复框架。核心思想是分两个阶段填充未知区域：将边缘外推到未知区域，然后在扩展边缘划分的每个子区域中填充未知像素值。Canny边缘检测和线性边缘扩展分别用于识别和扩展边缘到未知区域，然后在每个子区域内回归以预测未知像素值。两种不同的基于 K 近邻和支持向量机的回归模型用于预测未知像素值。所提出的框架具有无需对任何图像数据集进行事先训练即可进行修复的优势。对具有对比失真的不同图像进行的广泛实验证明了所提出框架的鲁棒性，详细的比较分析表明，所提出的技术优于现有的最先进的图像修复方法。最后，将所提出的技术应用于遭受易感伪影的 MRI 图像，以说明所提出工作的实际用途。