Abstract Depth image super-resolution (DISR) is an effective solution to improve the quality of depth images captured by real world low-cost cameras. In this paper, we propose a multi-scale symmetric network with the correlation-controlled color guidance block (CCGB) for DISR. The proposed network consists of two multi-scale sub-networks to respectively provide guidance and estimate depth. A symmetric unit (SU), which is a mini-encoder-decoder structure with residual learning, is designed and used as a basic network atom. The encoder part in SU aims to extract essential features, while the decoder part works to restore edge details. The way the SU processes information matches well with the textureless and sharp-edge characteristics of depth images. The two sub-networks present a high-level symmetric structure connected by dense guidance links in between. Based on the correlation analyses between the two sub-networks, each guidance link will transfer information trough a CCGB designed to implement channel-wise re-weighting mechanism. The accurate color guidance from CCGB helps avoiding artifacts introduced by non-co-occurrence of depth discontinuities and color edges. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art DISR works.

中文翻译：

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使用相关控制颜色引导和多尺度对称网络的深度图像超分辨率

摘要 深度图像超分辨率（DISR）是提高现实世界低成本相机捕获的深度图像质量的有效解决方案。在本文中，我们为 DISR 提出了一种具有相关控制颜色引导块 (CCGB) 的多尺度对称网络。所提出的网络由两个多尺度子网络组成，分别提供指导和估计深度。对称单元 (SU) 是具有残差学习的小型编码器-解码器结构，被设计并用作基本网络原子。SU 中的编码器部分旨在提取基本特征，而解码器部分则用于恢复边缘细节。SU 处理信息的方式与深度图像的无纹理和锐利边缘特征非常匹配。这两个子网络呈现出一种高级对称结构，由其间的密集引导链接连接。基于两个子网之间的相关性分析，每个引导链路将通过旨在实现信道重加权机制的 CCGB 传输信息。CCGB 的准确颜色指导有助于避免因深度不连续性和颜色边缘的非同时出现而引入的伪影。实验结果证明了所提出的方法优于几种最先进的 DISR 作品。CCGB 的准确颜色引导有助于避免因深度不连续性和颜色边缘的非同时出现而引入的伪影。实验结果证明了所提出的方法优于几种最先进的 DISR 作品。CCGB 的准确颜色指导有助于避免因深度不连续性和颜色边缘的非同时出现而引入的伪影。实验结果证明了所提出的方法优于几种最先进的 DISR 作品。