Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ hand-crafted operators to decompose the high-frequency (HF) and low-frequency (LF) ingredients from low-resolution depth maps and guide the HF ingredients by directly concatenating them with image features. However, the hand-designed operators usually cause inferior HF maps (e.g., distorted or structurally missing) due to the diverse appearance of complex depth maps. Moreover, the direct concatenation often results in weak guidance because not all image features have a positive effect on the HF maps. In this paper, we develop a recurrent structure attention guided (RSAG) framework, consisting of two important parts. First, we introduce a deep contrastive network with multi-scale filters for adaptive frequency-domain separation, which adopts contrastive networks from large filters to small ones to calculate the pixel contrasts for adaptive high-quality HF predictions. Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery. In addition, we fuse the features of HF maps to enhance the edge structures in the decomposed LF maps. Extensive experiments show that our approach obtains superior performance compared with state-of-the-art depth super-resolution methods.

翻译：图像引导是深度超分辨率的有效策略。通常，现有方法采用手工算子从低分辨率深度图中分解高频（HF）和低频（LF）成分，并通过直接拼接的方式将HF成分与图像特征进行引导。然而，由于复杂深度图的多样性外观，手工设计的算子通常导致HF图质量较差（例如，失真或结构缺失）。此外，直接拼接往往导致弱引导，因为并非所有图像特征都对HF图有正向作用。本文提出了一种循环结构注意力引导（RSAG）框架，包含两个重要部分。首先，我们引入了一种具有多尺度滤波器的深度对比网络，用于自适应频域分离，该网络从大滤波器到小滤波器逐步采用对比网络计算像素对比度，以实现自适应的高质量HF预测。其次，我们提出了一种循环结构注意力块，取代了粗略的拼接引导，通过迭代利用最新深度估计结果和图像特征共同选择清晰图案和边界，旨在为精确深度恢复提供精细引导。此外，我们融合了HF图的特征以增强分解后LF图中的边缘结构。大量实验表明，与当前最先进的深度超分辨率方法相比，本方法取得了优越的性能。