English(EN) Stabilizing Illumination Conditioning in SSM-Based Low-Light Image Enhancement with Bottleneck Prompt Sidecars

新方法稳定SSM图像增强中的照明条件

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种新方法，用于稳定基于状态空间模型（SSM）的低光图像增强中的照明条件。该方法利用瓶颈PromptAdapter将照明先验注入SSM骨干网络，而无需直接修改核心模型。在LOL-v1和LOLv2等数据集上进行测试，PromptAdapter在PSNR和SSIM指标上显示出显著的改进，尤其是在真实世界的低光图像上。 AI

影响这项研究为使用先进的AI模型增强低光图像提供了一种更稳定有效的方法，有可能改进摄影、监控和自主系统中的应用。

排序理由详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍图像增强新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Wei Chen Ng, Qiyi Xie, Jian Ding Tan, Yan Kang Lee, Abdulrab Hakim Qaid Abdullah, Wei Hown Tee · 2026-10-19 04:00

使用基于SSM的瓶颈提示侧车稳定光照条件进行低光图像增强

arXiv:2610.22958v1 Announce Type: cross Abstract: Low-light image enhancement (LLIE) requires more than brightness amplification because real low-light images often exhibit spatially varying illumination, amplified noise, color distortion, and local detail loss. Recent SSM- and M…

本文如何被排名

报道来源 [1]

使用基于SSM的瓶颈提示侧车稳定光照条件进行低光图像增强

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