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Chengcheng Jiang

Publications and source records attributed to Chengcheng Jiang.

2 recordsLinked to original sources

Resonant Raman scattering in bilayer 3R-MoS$_{2}$

Raman scattering is a powerful spectroscopic technique widely employed to investigate light-matter interactions and lattice dynamics in two-dimensional materials. Here, we investigate the temperature-dependent resonant Raman response of bilayer 3R-MoS$_2$. The study combines multi-wavelength Raman spectroscopy, photoluminescence measurements, and density functional theory calculations to track the evolution of excitonic transitions and resonance conditions. We observe contributions from both zone-centre and finite-momentum phonons, a pronounced quenching of the Stokes intensity at low temperatures followed by saturation, the emergence of anti-Stokes scattering above 130~K, and a strong deviation of the effective phonon temperature from the lattice temperature induced by resonance effects. These results demonstrate that the Raman response is governed by the interplay between incoming and outgoing resonance processes, providing deeper insight into exciton-phonon coupling in van der Waals materials.

cond-mat.mtrl-sci

Halo Separation-guided Underwater Multi-scale Image Restoration

Underwater images captured by Autonomous Underwater Vehicles (AUVs) are inevitably affected by artificial light sources, which often produce halos in the foreground of the camera and seriously interfere with the quality of the image. The existing underwater image enhancement methods fail to fully consider this key problem, and the robustness of processing images under artificial light scenes is poor. In practical applications, since underwater image enhancement itself is a very challenging task, the influence of artificial light sources will lead to serious degradation of image performance and affect subsequent vision tasks. In order to effectively deal with this problem, this paper designs a single halo image correction method based on an iterative structure. The network is mainly divided into two sub-networks, one is the halo layer separation sub-network which aims to separate the halo by gradient minimization, and the other is the multi-scale recovery sub-network which aims to recover the image information masked by halo. The UIEB and EUVP synthetic datasets are used for training to ensure that the network can fully learn the characteristics and laws of underwater halo images. Then a large number of halo images taken in an underwater environment with real artificial light are collected for testing. In addition, the brightness distribution characteristics of underwater halo images are analyzed and the radial gradient is introduced to constraint eliminate halo to improve the effect of underwater image restoration.

cs.CV