In this work, we present an end-to-end depth map super-resolution method based on CNN. Standing on a residual learning architecture, the proposed network learns ...
Standing on a residual learning archi- tecture, the proposed network learns joint features to get a high- resolution (HR) depth map from a low-resolution (LR) ...
This work presents an end-to-end depth map super-resolution method based on CNN and generates an edge-attention map from the associated HR color images as a ...
Image guidance is an effective strategy for depth super- resolution. Generally, most existing methods employ hand- crafted operators to decompose the high- ...
Depth super-resolution via fully edge-augmented guidance. record by Jingyu Yang • Depth super-resolution via fully edge-augmented guidance. Jingyu Yang, Hao ...
This paper proposes a depth map continuous SR (DCSR) framework that is able to achieve resolution adaptation at arbitrary SR ratios.
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Edge-Guided Depth Image Super-Resolution Based on KSVD pdf, Access, P ; Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine ...
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Depth Super-Resolution via Deep Controllable Slicing Network. ACM ... Depth super-resolution via fully edge-augmented guidance. VCIP 2017: 1-4.
FEAG[Paper]: Depth Super-Resolution via Fully Edge-Augmented Guidance (IEEE Visual Communications and Image Processing 2017), Jingyu Yang, Hao Lan, Xiaolin Song ...
Feb 25, 2022 · We propose a multi-scale residual deep network for depth map super-resolution. A cascaded transformer module incorporates high-resolution ...