Depth image super-resolution reconstruction based on a modified joint trilateral filter

Royal Society Open Science
Dongsheng ZhouXiaopeng Wei

Abstract

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.

References

Jan 12, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Guy GilboaYehoshua Y Zeevi
May 21, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jianchao YangYi Ma
Nov 26, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jun XieMing-Ting Sun
Jan 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Chao DongXiaoou Tang
Jan 28, 2017·IEEE Transactions on Cybernetics·Kai-Han LoKai-Lung Hua

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