Abstract
This article proposes an efficient wavelet-based depth video denoising approach based on a multihypothesis motion estimation aimed specifically at time-of-flight depth cameras. We first propose a novel bidirectional block matching search strategy, which uses information from the luminance as well as from the depth video sequence. Next, we present a new denoising technique based on weighted averaging and wavelet thresholding. Here we take into account the reliability of the estimated motion and the spatial variability of the noise standard deviation in both imaging modalities. The results demonstrate significantly improved performance over recently proposed depth sequence denoising methods and over state-of-the-art general video denoising methods applied to depth video sequences.
Highlights
The impressive quality of user perception of multimedia content has become an important factor in the electronic entertainment industry
The luminance image is less noisy, which facilitates the search for similar blocks. We have confirmed this experimentally by calculating peak signal-to-noise ratio (PSNR) of depth and luminance measurements, using ground truth images obtained by temporal averaging of the 200 static frames
5 Conclusions and future work In this article, we have presented a method for removing spatially variable and signal dependent noise in depth images acquired using a depth camera based on the time-of-flight principle
Summary
The impressive quality of user perception of multimedia content has become an important factor in the electronic entertainment industry. Depth images have other important applications in the assembly and inspection of industrial products, autonomous robots interacting with humans and real objects, intelligent transportation systems, biometric authentication and in biomedical imaging, where they have an important role in compensating for unwanted motion of patients during imaging These applications require even better accuracy of depth imaging than in the case of 3D TV, since the successful operation of various classification or motion analysis algorithms depends on the quality of input depth features. A large number of methods have been proposed for spatio-temporal noise reduction in TOF images and similar imaging modalities, based on other 3D scanning techniques. We propose a new method for denoising depth image sequences, taking into account information from the associated luminance sequences. The luminance image is less noisy, which facilitates the search for similar blocks We have confirmed this experimentally by calculating peak signal-to-noise ratio (PSNR) of depth and luminance measurements, using ground truth images obtained by temporal averaging of the 200 static frames.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
