Phase unwrapping is a critical step in fringe projection profilometry, essential for achieving accurate and efficient three-dimension (3D) imaging. Temporal phase unwrapping is the most widely utilized to improve robustness and the reconstruction quality. Unfortunately, due to abrupt phase discontinuities at boundaries, misalignment between the wrapped phases, and unreliable shadow regions, fringe order errors may occur. To address these challenges, this study presents a generalized multi-feature-guided progressive order correction algorithm (GMP-OCA) for high-quality 3D imaging. The algorithm integrates global coarse detection, incremental line-wise optimization, and regional precision scanning to progressively correct fringe orders. Static and dynamic experimental results demonstrate that GMP-OCA effectively eliminates the systematic errors inherent in various phase unwrapping methods, producing high-quality 3D imaging results.

Convolutional neural networks (CNNs) have recently achieved outstanding performance for hyperspectral (HS) and multispectral (MS) image fusion. However, CNNs cannot explore the long-range dependence for HS and MS image fusion because of their local receptive fields. To overcome this limitation, a transformer is proposed to leverage the long-range dependence from the network inputs. Because of the ability of long-range modeling, the transformer overcomes the sole CNN on many tasks, whereas its use for HS and MS image fusion is still unexplored. In this article, we propose a spectral-spatial transformer (SST) to show the potentiality of transformers for HS and MS image fusion. We devise first two branches to extract spectral and spatial features in the HS and MS images by SST blocks, which can explore the spectral and spatial long-range dependence, respectively. Afterward, spectral and spatial features are fused feeding the result back to spectral and spatial branches for information interaction. Finally, the high-resolution (HR) HS image is reconstructed by dense links from all the fused features to make full use of them. The experimental analysis demonstrates the high performance of the proposed approach compared with some state-of-the-art (SOTA) methods.

Recently, deep learning (DL)-based industrial applications have attracted broad attention due to their advanced performance. However, the limited computational resource in portable devices always makes big DL models inapplicable in the industry. DL-based single-image super-resolution also encounters this problem because of its large computations. Besides, most lightweight convolutional-neural-network-based methods utilize features insufficiently, which restricts their capability for industrial reconstruction. To alleviate this problem, we present a progress interaction-learning network (PILN) to refine features at different levels: at the global level, we employ a progressive interaction-learning strategy to integrate hierarchical features in temporal and spatial dimensions; at the mediate level, enhanced interaction-learning units, adopting the enhanced interactive study, significantly boost the reconstruction performance; at the local level, employing pixelwise learning, residual cells are raised to search for an optimal information flow by weight distribution. Extensive experiments demonstrate that the PILN outperforms other state-of-the-art methods.

Convolutional neural networks (CNNs) have achieved impressive performance for hyperspectral (HS) and multispectral (MS) image fusion in recent years. They extract features by local filters, which is limited to explore long-range dependency in input images. However, long-range dependence is an import cue for HS and MS image fusion, as it contributes to exploration of spatial self-similarity and spectral dependence. To take advantage of long-range dependence, we propose a spectral-spatial transformer (SST) for MS and HS image fusion. The experimental results demonstrate the high performance of the proposed approach compared to some state-of-the-art methods.