Low-light Image Enhancement Based on Multi-scale Wavelet U-Net

The real-time images collected by imaging systems in low illumination environments suffer from low illumination, severe noise and poor visual effects. To improve the image quality in low light environments, this study proposes a low-light image enhancement method based on a multi-scale wavelet U-Net. This method uses multi-level encoders and decoders to construct a U-Net and introduces the wavelet transform to develop a unit for frequency decomposition of features. This improves the perception of illumination and texture by separating high-frequency and low-frequency information. Multi-scale perceived loss is designed to guide the learned mapping of step-by-step reconstruction from low-frequency information to high-frequency information, thereby optimizing the convergence and performance of the network. Finally, the proposed method and comparison methods are tested on the LOL, LIME, NPE, MEF, DICM and VV datasets. The experimental results demonstrate that the proposed method can effectively brighten images, suppress image noise and texture loss, and improve the PSNR, SSIM, LOE and NIQE metrics. The proposed method exhibits better performance than other comparison algorithms in subjective and objective evaluation.