Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target’s surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.

Visual-tactile sensors that use a camera to capture the deformation of a soft gel layer have become popular in recent years. However, these sensors have a limited receptive field, which can hinder their ability to perceive tactile information effectively. In this letter, we propose a novel visual-tactile sensor named GelFinger that closely resembles the human finger and is well-suited for detecting various complex surfaces. The GelFinger sensor is equipped with an embedded miniature motor that allows for the adaptation of the camera pose and the scanning of a large contact area. During the detection process, the camera rotates to multiple angles to capture the tactile image of the contact area. To stitch together the tactile images obtained at different camera poses, we use an As-Projective-As-Possible image stitching algorithm to form a global view of the contact. We demonstrate the effectiveness of the GelFinger sensor in assessing large surfaces by using it to reconstruct curved crack outlines. Comparative experimental results show that the proposed sensor can effectively detect cracks and has the potential to assist humans in detecting defects on curved surfaces of infrastructure such as pipelines.

Foveated imaging, which has the ability to provide overall situational awareness over a large field of view and high-resolution perception of local details, has significant advantages in many specific applications. However, existing artificially foveated imaging systems are complex, bulky, and expensive, and the flexibility of the fovea specifically has many limitations. To overcome these deficiencies, this paper proposes a method for foveated imaging by collecting multiple partially overlapping sub-fields of view. To capture the above special sub-fields of view, we propose a high-efficiency algorithm based on the characteristics of the field of view deflected by the Risley-prism and aimed at solving the prism rotation angles. In addition, we prove the reliability of the proposed algorithm by cross-validation with the particle swarm optimization algorithm. The experimental results show that the proposed method can achieve flexible foveated imaging using a single Risley-prism imaging system.

Although multispectral and hyperspectral imaging acquisitions are applied in numerous fields, the existing spectral imaging systems suffer from either low temporal or spatial resolution. In this study, a new multispectral imaging system—camera array based multispectral super resolution imaging system (CAMSRIS) is proposed that can simultaneously achieve multispectral imaging with high temporal and spatial resolutions. The proposed registration algorithm is used to align pairs of different peripheral and central view images. A novel, super-resolution, spectral-clustering-based image reconstruction algorithm was developed for the proposed CAMSRIS to improve the spatial resolution of the acquired images and preserve the exact spectral information without introducing false information. The reconstructed results showed that the spatial and spectral quality and operational efficiency of the proposed system are better than those of a multispectral filter array (MSFA) based on different multispectral datasets. The PSNR of the multispectral super-resolution images obtained by the proposed method were respectively higher by 2.03 and 1.93 dB than those of GAP-TV and DeSCI, and the execution time was significantly shortened by approximately 54.55 s and 9820.19 s when the CAMSI dataset was used. The feasibility of the proposed system was verified in practical applications based on different scenes captured by the self-built system.

The significant absorption and scattering of light during its propagation in water severely degrade the quality of underwater imaging, presenting challenges for developing high-precision 3D imaging techniques based on optical methods. Polarization imaging has demonstrated effectiveness in mitigating the effects of scattering, making it a valuable approach for underwater imaging. Additionally, the polarization state of reflected light can be utilized for surface normal estimation and 3D shape reconstruction. This paper presents a learning-based method for 3D shape reconstruction of underwater targets using shape from polarization techniques. To address the lack of publicly available datasets for underwater polarization 3D imaging, we have developed a data acquisition system that simulates Jerlov Type I water conditions, creating a dataset of underwater polarized images along with corresponding ground truth surface normal images. Furthermore, we propose a network framework based on Attention U²Net for the 3D reconstruction of underwater polarized images. This framework is designed to capture detailed texture information of underwater targets and incorporates an effective polarization representation to resolve azimuthal ambiguity, thus enhancing the accuracy of underwater 3D imaging. Experimental results demonstrate that our method effectively addresses azimuthal ambiguity, reduces texture loss during reconstruction, and improves the accuracy of surface normal estimation, achieving superior performance compared to existing methods.