Title: Image Inpainting by Adaptive Fusion of Variable Spline Interpolations Show Chinese title

Title: 基于自适应融合可变样条插值的图像修复

Abstract: There are many methods for image enhancement. Image inpainting is one of them which could be used in reconstruction and restoration of scratch images or editing images by adding or removing objects. According to its application, different algorithmic and learning methods are proposed. In this paper, the focus is on applications, which enhance the old and historical scratched images. For this purpose, we proposed an adaptive spline interpolation. In this method, a different number of neighbors in four directions are considered for each pixel in the lost block. In the previous methods, predicting the lost pixels that are on edges is the problem. To address this problem, we consider horizontal and vertical edge information. If the pixel is located on an edge, then we use the predicted value in that direction. In other situations, irrelevant predicted values are omitted, and the average of rest values is used as the value of the missing pixel. The method evaluates by PSNR and SSIM metrics on the Kodak dataset. The results show improvement in PSNR and SSIM compared to similar procedures. Also, the run time of the proposed method outperforms others. Show Chinese abstract

Abstract: 有许多图像增强方法。 图像修复是其中一种，可用于划痕图像或通过添加或删除对象来编辑图像的重建和修复。 根据其应用，提出了不同的算法和学习方法。 在本文中，重点是增强旧的历史划痕图像。 为此，我们提出了一种自适应样条插值。 在此方法中，对于丢失块中的每个像素，考虑四个方向的不同数量的邻居。 在以前的方法中，预测位于边缘上的丢失像素是一个问题。 为了解决这个问题，我们考虑水平和垂直边缘信息。 如果像素位于边缘上，则使用该方向的预测值。 在其他情况下，忽略不相关的预测值，并将其余值的平均值作为缺失像素的值。 该方法通过PSNR和SSIM指标在Kodak数据集上进行评估。 结果表明，与类似过程相比，PSNR和SSIM有所提高。 此外，所提出方法的运行时间优于其他方法。