To overcome hardware limitations in commercially available depth sensors which result in low-resolution depth maps, depth map super-resolution (DMSR) is a practical and valuable computer vision task. DMSR requires upscaling a low-resolution (LR) depth map into a high-resolution (HR) space. Joint image filtering for DMSR has been applied using spatially-invariant and spatially-variant convolutional neural network (CNN) approaches. In this project, we propose a novel joint image filtering DMSR algorithm using a Swin transformer architecture. Furthermore, we introduce a Nonlinear Activation Free (NAF) network based on a conventional CNN model used in cutting-edge image restoration applications and compare the performance of the techniques. The proposed algorithms are validated through numerical studies and visual examples demonstrating improvements to state-of-the-art performance while maintaining competitive computation time for noisy depth map super-resolution.

翻译：为克服商用深度传感器硬件限制导致的低分辨率深度图问题，深度图超分辨率（DMSR）是一项实用且有价值的计算机视觉任务。DMSR需将低分辨率（LR）深度图上采样至高分辨率（HR）空间。目前，基于空间不变与空间自适应卷积神经网络（CNN）的方法已用于联合图像滤波的DMSR任务。在本项目中，我们提出一种基于Swin transformer架构的新型联合图像滤波DMSR算法。此外，我们引入了一种基于传统CNN模型的无非线性激活（NAF）网络（该网络常用于前沿图像复原应用），并比较了各技术的性能。通过数值实验与视觉示例验证了所提算法的有效性，结果表明其在保持具有竞争力的计算时间的同时，实现了对含噪深度图超分辨率领域最先进性能的改进。