Shuyi Cao | Searcharxiv

arXiv2026

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↗

arXiv2025

Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning

We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions into physically meaningful components. These data-driven interpretations reveal periodic trends and stability principles governing transition metals. We apply the model to single-atom alloys (SAAs) to assess their thermodynamic resilience against two destabilizing processes: agglomeration (adatom clustering) and segregation (migration into the subsurface). Our analysis shows that these phenomena are governed by distinct physical factors-agglomeration is primarily influenced by localized d-orbital coupling, while segregation is dictated by delocalized effects such as wavefunction renormalization. This model thus serves as an explainable AI tool for understanding and guiding the design of stable TMAs, with implications for catalysis and materials discovery.

cond-mat.mtrl-sci↗