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.

本研究提出了一种基于 Swin 变换器架构和非线性激活函数自由网络的联合图像滤波深度图超分辨率算法，并通过数值研究和视觉示例证明了其在保持竞争计算时间的同时提高了最先进性能。