Low-light image enhancement is a crucial visual task, and many unsupervised
methods tend to overlook the degradation of visible information in low-light
scenes, which adversely affects the fusion of complementary information and
hinders the generation of satisfactory results. To address this, our study
introduces ``Enlighten-Your-Voice'', a multimodal enhancement framework that
innovatively enriches user interaction through voice and textual commands. This
approach does not merely signify a technical leap but also represents a
paradigm shift in user engagement. Our model is equipped with a Dual
Collaborative Attention Module (DCAM) that meticulously caters to distinct
content and color discrepancies, thereby facilitating nuanced enhancements.
Complementarily, we introduce a Semantic Feature Fusion (SFM) plug-and-play
module that synergizes semantic context with low-light enhancement operations,
sharpening the algorithm's efficacy. Crucially, ``Enlighten-Your-Voice''
showcases remarkable generalization in unsupervised zero-shot scenarios. The
source code can be accessed from
this https URL

我们研究了一种名为 “Enlighten-Your-Voice” 的多模态增强框架，通过声音和文本命令创新地丰富用户交互，以解决低光场景中可见信息退化对互补信息融合和结果生成的不利影响，并呈现了在无监督零样本情境中引人注目的泛化能力。