Single-Exposure 3-D Measurement of High-Dynamic-Range Surfaces via Untrained Neural Network

Three-dimensional (3D) measurement for high-dynamic-range (HDR) surfaces is one of challenge issues in industry manufacturing. However, current HDR methods, including conventional methods and supervised-learning-based methods, generally make compromise in either the efficiency or the accuracy. To alleviate this compromise, we propose a generalized fringe enhancement method based on the untrained neural network (UNN) to achieve HDR measurement from only a single exposure. There are two important contributions in our work. First, to the best of our knowledge, we propose the first generalized UNN-based framework to solve the underexposure-overexposure hybrid issue. Without pre-training on any dataset, our framework can simultaneously achieve fringe denoising in the underexposure issue, and fringe inpainting in the overexposure issue. Second, we propose a sine regularization term to improve the inpainting quality in overexposed areas. Unlike existing methods that merely inpaint the corrupted areas based on their reliable adjacent areas, we utilize the unique sinusoid character of fringe to constrain the inpainting. Consequently, the robustness of our method can be effectively enhanced. Experiments for poorly illuminated scenes, high-reflection scenes, and their hybrid scenes demonstrate the proposed single-exposure method can substantially eliminate the measurement error (0.0603 mm vs 4.1775 mm) compared with the direct measurement.