Cutting-Edge Techniques for Depth Map Super-Resolution
Ryan Peterson Josiah Smith
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.