Guided depth super-resolution is a practical task where a low-resolution and noisy input depth map is restored to a high-resolution version, with the help of a high-resolution RGB guide image. Existing methods usually view this task as a generalized guided filtering problem that relies on designing explicit filters and objective functions, or a dense regression problem that directly predicts the target image via deep neural networks. These methods suffer from either model capability or interpretability. Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values. JIIF represents the target image domain with spatially distributed local latent codes extracted from the input image and the guide image, and uses a graph attention mechanism to learn the interpolation weights at the same time in one unified deep implicit function. We demonstrate the effectiveness of our JIIF representation on guided depth super-resolution task, significantly outperforming state-of-the-art methods on three public benchmarks. Code can be found at \url{https://git.io/JC2sU}.

翻译：引导深度超分辨率是一项实际任务,即借助高分辨率 RGB 指导图像,将低分辨率和噪音输入深度地图恢复到高分辨率版本。 现有方法通常将这项任务视为一个普遍引导过滤的问题,它依赖于设计清晰的过滤器和客观功能,或是一个通过深层神经网络直接预测目标图像的密集回归问题。 这些方法要么具有模型能力,要么具有可解释性。 受最近隐含神经表示方面的进展的启发,我们提议将导导超分辨率作为一个神经隐含图像内插问题,我们采取一般图像内插的形式,但使用新的联合隐含图像函数(JIIF)来学习内插权重和值。 JIF 代表目标图像域,使用空间分布的本地潜在代码,从输入图像和指南图像中提取,并使用图形关注机制,在同一时间学习内插权,一个统一的深度隐含性功能。 我们展示了我们的JIF代表在引导深度超分辨率任务上的有效性,大大优于状态/C2 3 公共基准上可找到代码。