2026-10-13T23:56:04Z https://www.scidb.cn/oai

10.57760/sciencedb.j00240.00280 2026-08-04T10:57:18Z

2026-08-04 ICD-GAN doi:10.57760/sciencedb.j00240.00280 en To address the issues of detail loss and color distortion caused by the lack of prior constraints, as well as noise amplification induced by brightening in low-light image enhancement, this paper proposes an unpaired unsupervised low-light image enhancement method based on illumination curve mapping and structure-preserving denoising. In the generation stage, a deep-feature-driven iterative nonlinear illumination curve mapping mechanism is designed to improve detail enhancement capabilities and suppress color distortion. To guarantee the parameter estimation required for curve mapping, a multi-scale feature extraction module and a noise-robust convolutional attention module are developed to alleviate the limitations of global illumination perception and noise interference during parameter estimation. Furthermore, a half-instance normalization mechanism integrated with zero-initialized dynamic residuals is designed to suppress noise amplification caused by brightening. In the discriminator stage, a global-local dual-branch architecture is employed to jointly supervise the global illumination distribution and local textures of the image, and a multi-dimensional joint constraint loss, incorporating channel mapping consistency and exposure control, is formulated. low-light image enhancement; illumination curve mapping; structure-preserving denoising; joint constraint loss Shao Tianxiang RESTRICTED dataset Science Data Bank