Implementation of four kriging models for depth inpainting
Yossawee Keaomanee, Apichat Heednacram, Prakarnkiat Youngkong
Abstract
Image inpainting is a process of reconstructing the missing pixels by inferencing information from the known part of an image. This paper aims to increase the precision of depth inpainting by proposing four models based on kriging techniques. Our four kriging models are based on two different semivariance models (exponential model and spherical model) and two different color-similarity functions. The inpainting algorithm is designed to extract both color and depth information of RGB-D images. For efficiency assessment, we look at Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), and Peak Signal to Noise Ratio (PSNR) of the reconstructed images. We then make a performance comparison with the previous six conventional methods. Finally, we show that our implemented models outperform the conventional ones. The accuracy of our four kriging models is competitive, and the PSNR values lie between 30.81 to 45.46 dB.