An Iterative attention normalization flow (IANFlow) network is proposed to address the problem of insufficient feature fusion between network layers and lack of accurate localization and acquisition of high-frequency features, as well as the problem of uncertain mapping between low-light images and multiple normal-exposure images. The iterative attention module uses spatial and channel attention to localize the high-frequency feature regions of the input feature maps and then performs feature acquisition, which prompts the deeper feature maps to contain more high -frequency features through incremental hierarchical localization and fusion; the reversible normalization flow module learns the complex conditional distributions between low-light images and normal-exposure images as well as minimizes the negative log-likelihood (NLL) to establish the uncertainty in mappings between a low-light image and a reference image. one-to-many mapping. The peak signal-to-noise ratio (PSNR) of the IANFlow network is improved by 1.1 dB, 1.27 dB, and 2.14 dB when comparing the LLFlow network on each of the three datasets.