A New Virtual View Rendering Method Based on Depth Image

Jiufei, Xue; Xi, Mian; Li, Dongxiao; Zhang, Ming

doi:10.1109/apwcs.2010.43

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…By comparing the similarity with the image-depth patches, different levels of enhancements are utilised to enhance the visual quality of the image, as well as the sharpness of the depth map. The method does not require complicated training parameters and is suitable for practical depth-based image rendering applications [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Precise depth map upsampling and enhancement based on edge‐preserving fusion filters

Chang

Yang

2018

IET Computer Vision

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A texture image plus its associated depth map is the simplest representation of a three-dimensional image and video signals and can be further encoded for effective transmission. Since it contains fewer variations, a depth map can be coded with much lower resolution than a texture image. Furthermore, the resolution of depth capture devices is usually also lower. Thus, a low-resolution depth map with possible noise requires appropriate interpolation to restore it to full resolution and remove noise. In this study, the authors propose potency guided upsampling and adaptive gradient fusion filters to enhance the erroneous depth maps. The proposed depth map enhancement system can successfully suppress noise, fill missing values, sharpen foreground objects, and smooth background regions simultaneously. Their experimental results show that the proposed methods perform better in terms of both visual and subjective metrics than the classic methods and achieve results that are visually comparable with those of some time-consuming methods.

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Section: Introductionmentioning

confidence: 99%

Precise depth map upsampling and enhancement based on edge‐preserving fusion filters

Chang

Yang

2018

IET Computer Vision

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“…2(g)]. A more advanced algorithm was proposed by Jiufei et al [10]. They fill the uncovered area via texture synthesis and are able to show that better visual results can be achieved compared to the previously described methods.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned before [6], [10], texture synthesis [22]- [30] is an appropriate technique for filling missing image regions with known information. This method operates in parametric [23]- [25] or nonparametric [22], [26]- [30] modes.…”

Section: Introductionmentioning

confidence: 99%