Image dehazing combining polarization properties and deep learning.

In order to solve the problems of color shift and incomplete dehazing after image dehazing, this paper proposes an improved image self-supervised learning dehazing algorithm that combines polarization characteristics and deep learning. First, based on the YOLY network framework, a multiscale module and an attention mechanism module are introduced into the transmission feature estimation network. This enables the extraction of feature information at different scales and allocation of weights, and effectively improves the accuracy of transmission map estimation. Second, a brightness consistency loss based on the YCbCr color space and a color consistency loss are proposed to constrain the brightness and color consistency of the dehazing results, resolving the problems of darkened brightness and color shifts in dehazed images. Finally, the network is trained to dehaze polarized images based on the atmospheric scattering model and loss function constraints. Experiments are conducted on synthetic and real-world data, and comparisons are made with six contrasting dehazing algorithms. The results demonstrate that, compared to the contrastive dehazing algorithms, the proposed algorithm achieves PSNR and SSIM values of 23.92 and 0.94, respectively, on synthetic image samples. For real-world image samples, color restoration is more authentic, contrast is higher, and detailed information is richer. Both subjective and objective evaluations show significant improvements. This validates the effectiveness and superiority of the proposed dehazing algorithm.

Calibration of a Catadioptric System and 3D Reconstruction Based on Surface Structured Light.

In response to the problem of the small field of vision in 3D reconstruction, a 3D reconstruction system based on a catadioptric camera and projector was built by introducing a traditional camera to calibrate the catadioptric camera and projector system. Firstly, the intrinsic parameters of the camera and the traditional camera are calibrated separately. Then, the calibration of the projection system is accomplished by the traditional camera. Secondly, the coordinate system is introduced to calculate, respectively, the position of the catadioptric camera and projector in the coordinate system, and the position relationship between the coordinate systems of the catadioptric camera and the projector is obtained. Finally, the projector is used to project the structured light fringe to realize the reconstruction using a catadioptric camera. The experimental results show that the reconstruction error is 0.75 mm and the relative error is 0.0068 for a target of about 1 m. The calibration method and reconstruction method proposed in this paper can guarantee the ideal geometric reconstruction accuracy.

N-doped graphene encapsulated MoS2nanosphere composite as a high-performance anode for lithium-ion batteries.

MoS2is widely used in lithium-ion batteries (LIBs) due to its high capacity (670 mAh g-1) and unique two-dimensional structure. However, the further application was limited of MoS2as anode materials suffer from its volume expansion and low conductivity. In this work, N-doped graphene encapsulated MoS2nanosphere composite (MoS2@NG) were prepared and its unique sandwich structure containing abundant mesopores and defects can efficiently enhance reaction kinetics. The MoS2@NG electrode shows a reversible capacity of 975.9 mAh g-1at 0.1 A g-1after 100 cycles, and a reversible capacity of 325.2 mAh g-1is still maintained after 300 cycles at 5 A g-1. In addition, the MoS2@NG electrode exhibites an excellent rate performance benefiting from the electrochemical properties dominated by capacitive behavior. This suggests that MoS2@NG composite can be used as potential anode materials for LIBs.