Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.
随着电影对极致沉浸式视听体验的发展需求,沉浸式声场记录和重放技术日显重要。本文围绕电影音频制作技术中的声场记录和重放问题,介绍了基于球麦克风阵列的高阶高保真立体声(Higher Order Ambisonics,HOA)分析技术,并针对球麦克风阵列球谐分解中的低频噪声与高频混叠问题,以及双耳重放技术中的阶数受限问题,给出了相应解决方案,研究表明所提方案可为观众提供更真实、更具沉浸感的声场重放效果,提升了观影体验,在电影音频制作中具有广阔的应用前景。