论文标题
图像通过多条样条插值插图
Image Inpainting by Multiscale Spline Interpolation
论文作者
论文摘要
恢复图像的缺失区域是一个称为图像插入的任务。根据缺失区域的形状，文献中显示了不同的方法。该问题的挑战之一是提取能够带来更好结果的功能。实验结果表明，全球和本地特征都对此目的有用。在本文中，我们提出了一种使用本地和全局特征的多尺度图像介绍方法。该方法的第一步是确定我们需要使用多少尺度，这取决于缺失区域地图中线的宽度。然后，我们将自适应图像插入图像的损坏区域，并预测丢失的像素。每个量表都被覆盖，结果调整到原始尺寸。然后，投票过程将产生最终结果。提出的方法在划痕和折痕的受损图像上进行了测试。我们用来评估方法的指标是PSNR。平均而言，我们在某些现有的涂上方法方面取得了1.2 dB的改进。
Recovering the missing regions of an image is a task that is called image inpainting. Depending on the shape of missing areas, different methods are presented in the literature. One of the challenges of this problem is extracting features that lead to better results. Experimental results show that both global and local features are useful for this purpose. In this paper, we propose a multi-scale image inpainting method that utilizes both local and global features. The first step of this method is to determine how many scales we need to use, which depends on the width of the lines in the map of the missing region. Then we apply adaptive image inpainting to the damaged areas of the image, and the lost pixels are predicted. Each scale is inpainted and the result is resized to the original size. Then a voting process produces the final result. The proposed method is tested on damaged images with scratches and creases. The metric that we use to evaluate our approach is PSNR. On average, we achieved 1.2 dB improvement over some existing inpainting approaches.
