SynBF: A New Bilateral Filter for Postremoval of Noise From Synthesis Views in 3-D Video

Abstract

In 3-D video systems, noise in the texture and depth videos of reference views may not be removed (Scenario 1) or not fully removed (Scenario 2) by prefiltering methods before the view synthesis procedure. In these scenarios, the noise is transferred to the generated synthesis view. After investigating the noise model of the synthesis view, we conclude that the noise in the synthesis view not only causes fluctuation in the photometric values of pixels in the range domain but also additionally shifts the positions of neighbor pixels in the spatial domain compared to that in natural images. It consequently damages the textural content near edges in the synthesis view, for which the popular local filters of natural images, that is, the bilateral filter (BF) and the guided filter, do not work well. In this paper, we develop a new local filter for the synthesis view (named SynBF) after it has been generated, which has a similar expression as that of the BF but not the exact same weight terms. On one hand, the spatial term in the classical BF is directly reused due to its robustness to noise, which gives high weights to spatially closed pixels of to-be-filtered pixels. On the other hand, a reliability term is designed that gives high weights to pixels that are unlikely to be affected by noise. It is inspired by the finding that not all pixels are significantly affected by noise in the synthesis view. In this way, true edge profiles are protected in the filtering process. Experiments are conducted on a set of synthesis views for both scenarios above and compared to the two local filters, which verifies its effectiveness in removing noise and protecting edge profiles. The proposed method can be considered as a supplement to prefiltering methods of texture/depth videos in 3-D video systems.