Shahrood University of Technology Journal of AI and Data Mining 2322-5211 7 4 2019 11 01 Depth Improvement for FTV Systems Based on the Gradual Omission of Outliers 563 574 1464 10.22044/jadm.2019.7278.1864 EN H. Hosseinpour Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran. Seyed A. Moosavie Nia Department of Computer Engineering, K.N Toosi University, Tehran, Iran. M. A. Pourmina Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran. Journal Article 2018 07 17 Virtual view synthesis is an essential part of computer vision and 3D applications. A high-quality depth map is the main problem with virtual view synthesis. Because as compared to the color image the resolution of the corresponding depth image is low. In this paper, an efficient and confided method based on the gradual omission of outliers is proposed to compute reliable depth values. In the proposed method depth values that are far from the mean of depth values are omitted gradually. By comparison with other state of the art methods, simulation results show that on average, PSNR is 2.5dB (8.1 %) higher, SSIM is 0.028 (3%) more, UNIQUE is 0.021 (2.4%) more, the running time is 8.6s (6.1%) less and wrong pixels are 1.97(24.8%) less. https://jad.shahroodut.ac.ir/article_1464_d9e5d8d85b340efa1b183800aae20cc0.pdf