Contemporary Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast, achieving commendable results on specific datasets. Nevertheless, these approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. Insufficient constraints on complex pixel-wise mapping learning lead to overfitting to specific types of noise and artifacts associated with low-light conditions, reducing effectiveness in variable lighting scenarios. To this end, we first propose a method for estimating the noise level in low light images in a quick and accurate way. This facilitates precise denoising, prevents over-smoothing, and adapts to dynamic noise patterns. Subsequently, we devise a Learnable Illumination Interpolator (LII), which employs learnlable interpolation operations between the input and unit vector to satisfy general constraints between illumination and input. Finally, we introduce a self-regularization loss that incorporates intrinsic image properties and essential visual attributes to guide the output towards meeting human visual expectations. Comprehensive experiments validate the competitiveness of our proposed algorithm in both qualitative and quantitative assessments. Notably, our noise estimation method, with linear time complexity and suitable for various denoisers, significantly improves both denoising and enhancement performance. Benefiting from this, our approach achieves a 0.675dB PSNR improvement on the LOL dataset and 0.818dB on the MIT dataset on LLIE task, even compared to supervised methods.

翻译：当代低光照图像增强（LLIE）技术在保留图像细节与提升对比度方面取得了显著进展，在特定数据集上展现了令人瞩目的成果。然而，这些方法在高效抑制动态噪声并适应多样化低光照场景方面仍面临持续挑战。对复杂像素级映射学习施加的约束不足，导致模型过度拟合低光照条件下特定类型的噪声与伪影，从而降低了其在多变光照场景中的有效性。为此，我们首先提出一种快速准确估计低光照图像噪声水平的方法。该方法有助于实现精确去噪、防止过度平滑，并自适应动态噪声模式。其次，我们设计了一种可学习光照插值器（LII），通过在输入向量与单位向量之间执行可学习插值操作，以满足光照与输入之间的通用约束。最后，我们引入一种融合图像固有属性与基本视觉特征的自正则化损失，以引导输出结果符合人类视觉预期。综合实验表明，所提算法在定性及定量评估中均具有竞争力。值得注意的是，我们的噪声估计方法具有线性时间复杂度，适用于多种去噪器，显著提升了去噪与增强性能。得益于此，即使在面向LLIE任务时与有监督方法相比，我们的方法在LOL数据集上仍实现了0.675dB的PSNR提升，在MIT数据集上实现了0.818dB的提升。