arXiv subjects

Danni Huang

Publications and source records attributed to Danni Huang.

3 recordsLinked to original sources

arXiv2026

Vortex-mediated spin current injection into two-dimensional superconductor NbSe2

Injection of pure spin current into superconductors remains a major challenge in superconducting spintronics. Previous studies have primarily focused on spin-polarized quasiparticles and spin-triplet supercurrents, while vortices, ubiquitous topological defects in type-2 superconductors, have been theoretically proposed as alternative carriers of spin angular momentum, yet direct experimental evidence is still lacking. Here, we report the vortex-mediated spin current injection in NbSe2/LiAl2Fe3O8(LAFO) bilayer, an Ising superconductor/ferrimagnetic insulator heterostructure. Under an out-of-plane temperature gradient and an in-plane magnetic field, the NbSe2/LAFO bilayer shows a pronounced thermoelectric peak near the upper critical magnetic field, which has the opposite sign to the conventional vortex Nernst signal observed in a single-layer NbSe2. The sign reversal suggests that the vortex flow induced by spin current injection is opposite to the flow driven by the temperature gradient, which is consistent with theoretical mechanisms including spin-vorticity transmutation and the inverse vortex spin Hall effect. By mapping the field temperature phase diagram, we reveal that the spin current injection occurs exclusively in the vortex liquid phase of NbSe2. Absence of the thermoelectric signal above the superconducting transition temperature further rules out the quasiparticle contribution. Our results establish vortices as efficient carriers of spin information in superconductors, opening a new route towards vortex-mediated superconducting spintronic devices.

cond-mat.supr-con↗

arXiv2026

From Global to Granular: Revealing IQA Model Performance via Correlation Surface

Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs with small quality/MOS differences (related to $|Δ$MOS$|$). Such complementary behaviors are invisible under global metrics. Moreover, SRCC and PLCC are sensitive to test-sample quality distributions, yielding unstable comparisons across test sets. To address these limitations, we propose \textbf{Granularity-Modulated Correlation (GMC)}, which provides a structured, fine-grained analysis of IQA performance. GMC includes: (1) a \textbf{Granularity Modulator} that applies Gaussian-weighted correlations conditioned on absolute MOS values and pairwise MOS differences ($|Δ$MOS$|$) to examine local performance variations, and (2) a \textbf{Distribution Regulator} that regularizes correlations to mitigate biases from non-uniform quality distributions. The resulting \textbf{correlation surface} maps correlation values as a joint function of MOS and $|Δ$MOS$|$, providing a 3D representation of IQA performance. Experiments on standard benchmarks show that GMC reveals performance characteristics invisible to scalar metrics, offering a more informative and reliable paradigm for analyzing, comparing, and deploying IQA models. Codes are available at https://github.com/Dniaaa/GMC.

cs.CV↗

arXiv2025

The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement

There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches proposed to enhance low-light images, much less work has been dedicated to quality assessment and quality optimization of low-light enhancement. In this paper, to close the gap between enhancement and assessment, we propose a loop enhancement framework that produces a clear picture of how the enhancement of low-light images could be optimized towards better visual quality. In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures. The objective quality assessment measure plays a critical bridging role between visual quality and enhancement and is further incorporated in the optimization in learning the enhancement model towards perceptual optimally. Finally, we iteratively perform the enhancement and optimization tasks, enhancing the low-light images continuously. The superiority of the proposed scheme is validated based on various low-light scenes.

eess.IV↗