InvEn-Net: under-exposed image enhancement based on frequency adjustment

When images are captured in dark environments, they may be of low quality due to extreme lighting conditions and equipment limitations. The degradation of underexposed images includes high noise in dark areas, low visibility, and loss of details. Many enhancement methods have been proposed, but generally suffer from inconsistent color and incomplete detail, with artifacts appearing in extremely dark areas. In addition, these methods are computationally expensive due to the use of hierarchical processing methods. Therefore, an integrated, noise-sensitive approach is needed. This paper introduces the Invertible Enhancement Network (InvEn-Net), which uses the Invertible network and BM3D algorithms to separate the high-frequency and low-frequency components. The compositional relationship between the two components is learned based on the original image. The low-frequency components undergo color correction and partial detail supplementation using a U-shaped convolutional neural network (UNet). The prior distribution (Gaussian distribution) of the high-frequency components is resampled, finally leading to the restoration of the normally exposed image. Compared with various types of low light image enhancement methods, our framework can achieve competitive color consistency and structural similarity (SSIM). Ablation experiments justify our selection of model structural parameters.