Edge area constraint guided filter depth image super-resolution reconstruction algorithm

2021 ◽  
Vol 50 (1) ◽  
pp. 20200081-20200081
Author(s):  
武军安 Jun''an Wu ◽  
郭锐 Rui Guo ◽  
刘荣忠 Rongzhong Liu ◽  
柯尊贵 Zungui Ke ◽  
赵旭 Xu Zhao
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Guided Filter ◽  
Depth Image ◽  
Image Super Resolution

Edge area constraint guided filter depth image super-resolution reconstruction algorithm

2021 ◽  
Vol 50 (1) ◽  
pp. 20200081-20200081
Author(s):  
武军安 Jun''an Wu ◽  
郭锐 Rui Guo ◽  
刘荣忠 Rongzhong Liu ◽  
柯尊贵 Zungui Ke ◽  
赵旭 Xu Zhao
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Guided Filter ◽  
Depth Image ◽  
Image Super Resolution

scholarly journals Edge-Guided Depth Image Super-Resolution Based on KSVD

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 41108-41115
Author(s):  
Binhui Liu ◽  
Qiang Ling
Keyword(s):  
Super Resolution ◽  
Depth Image ◽  
Image Super Resolution

Discrete and continuous optimizations for depth image super-resolution

2012 ◽  
Author(s):  
Ouk Choi ◽  
Hwasup Lim ◽  
Byongmin Kang ◽  
Yong Sun Kim ◽  
Keechang Lee ◽  
...  
Keyword(s):  
Super Resolution ◽  
Depth Image ◽  
Image Super Resolution

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

2019 ◽  
Vol 6 (1) ◽  
pp. 181074 ◽  
Author(s):  
Dongsheng Zhou ◽  
Ruyi Wang ◽  
Xin Yang ◽  
Qiang Zhang ◽  
Xiaopeng Wei
Keyword(s):  
Super Resolution ◽  
Structural Similarity ◽  
Depth Image ◽  
High Quality ◽  
Depth Images ◽  
Trilateral Filter ◽  
Coding Method ◽  
Image Edge ◽  
Image Super Resolution ◽  
Sr Reconstruction

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.

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