论文标题
基于径向的GRNN分析非纹理图像插入
Radial Based Analysis of GRNN in Non-Textured Image Inpainting
论文作者
论文摘要
图像介入算法用于根据周围信息恢复图像的某些损坏或缺失的信息区域。本文提出的方法采用了基于径向的GRNN图像的分析。首先将受损的区域与其他区域隔离，然后按其大小排列，然后使用GRNN进行覆盖。神经网络的训练是使用不同的半径进行更好结果的。针对不同的基于回归的算法进行了比较分析。将总体结果与其他算法作为LS-SVM获得的结果进行了比较。
Image inpainting algorithms are used to restore some damaged or missing information region of an image based on the surrounding information. The method proposed in this paper applies the radial based analysis of image inpainting on GRNN. The damaged areas are first isolated from rest of the areas and then arranged by their size and then inpainted using GRNN. The training of the neural network is done using different radii to achieve a better outcome. A comparative analysis is done for different regression-based algorithms. The overall results are compared with the results achieved by the other algorithms as LS-SVM with reference to the PSNR value.
