Photometric Stereo Super Resolution via Complex Surface Structure Estimation
Han-nyoung Lee, Hak Gu Kim · IEEE Access · 2024
Photometric stereo, which derives per-pixel surface normals from shading cues, faces challenges in capturing high-resolution (HR) images in linear response systems. We address the representation of HR surface normals from low-resolution (LR) photometric stereo images. To represent fine details of the surface normal in the HR domain, we propose a novel plug-in high-frequency representation module named the Complex Surface Structure (CSS) estimator. When combined with a conventional photometric stereo model, CSS is capable of representing intricate surface structures in 2D Fourier space. We show that photometric stereo super-resolution (SR) with our CSS estimator provides high-fidelity surface normal representations in higher resolution from the LR inputs. Experiments demonstrate that our results are quantitatively and qualitatively better than those of the existing deep learning-based SR work.