Object phase-valid region segmentation method for FPP-based three-dimensional measurement.

In most existing studies based on fringe projector profilometry (FPP), the whole scenario is reconstructed, or the ideal experimental settings are established to segment the object easily. However, in real industrial scenarios, automated object detection and segmentation are essential to perform object-level measurement. To address the problem, a dual-wavelet feature interaction network (DWFI-Net) is developed in this paper to perform object phase-valid region segmentation, where both the background and shadow are removed. In our work, the modulation and wrapped phase maps are considered as inputs innovatively. The modulation maps provide abundant structures and textures, while the wrapped phase maps complement and enhance shadows and edges. An adaptive wavelet feature interaction (AWFI) module is presented to learn and fuse the features, where discrete wavelet transformation (DWT) is applied to decompose the features. An edge-aware discrete cosine transformation (EDCT) module is developed as a decoder, where the discrete cosine transformation (DCT) is applied to interpret the fused features. Qualitative and quantitative experiments are performed to verify the superiority of our DWFI-Net and its effectiveness on object-level three-dimensional measurement based on FPP.

Polarized reflection removal with difference feature attention guidance.

Reflection removal is of great significance for high-level computer vision tasks. Most existing methods separate reflections relying heavily on the quality of intermediate prediction or under certain special constraints. However, these methods ignore the inherent correlation between the background and reflection, which may lead to unsatisfactory results with undesired artifacts. Polarized images contain unique optical characteristics that can facilitate reflection removal. In this paper, we present, to the best of our knowledge, a novel two-stage polarized image reflection removal network with difference feature attention guidance. Specifically, our model takes multi-channel polarized images and Stokes parameters as input and utilizes the optical characteristics of reflected and transmitted light to alleviate the ill-posed nature. It adopts a simple yet effective two-stage structure that first predicts the reflection layer and then refines the transmission layer capitalizing on the special relationship between reflection and transmission light. The difference feature attention guidance module (DFAG) is elaborated to diminish the dependence on intermediate consequences and better suppress reflection. It mitigates the reflection components from the observation and generates the supplement and enhancement to the transmission features. Extensive experiments on the real-world polarized dataset demonstrate the superiority of our method in comparison to the state-of-the-art methods.