Recently, Fourier frequency information has attracted much attention in Low-Light Image Enhancement (LLIE). Some researchers noticed that, in the Fourier space, the lightness degradation mainly exists in the amplitude component and the rest exists in the phase component. By incorporating both the Fourier frequency and the spatial information, these researchers proposed remarkable solutions for LLIE. In this work, we further explore the positive correlation between the magnitude of amplitude and the magnitude of lightness, which can be effectively leveraged to improve the lightness of low-light images in the Fourier space. Moreover, we find that the Fourier transform can extract the global information of the image, and does not introduce massive neural network parameters like Multi-Layer Perceptrons (MLPs) or Transformer. To this end, a two-stage Fourier-based LLIE network (FourLLIE) is proposed. In the first stage, we improve the lightness of low-light images by estimating the amplitude transform map in the Fourier space. In the second stage, we introduce the Signal-to-Noise-Ratio (SNR) map to provide the prior for integrating the global Fourier frequency and the local spatial information, which recovers image details in the spatial space. With this ingenious design, FourLLIE outperforms the existing state-of-the-art (SOTA) LLIE methods on four representative datasets while maintaining good model efficiency. Notably, compared with a recent Transformer-based SOTA method SNR-Aware, FourLLIE reaches superior performance with only 0.31% parameters. Code is available at https://github.com/wangchx67/FourLLIE

Images captured in low-light conditions usually suffer from degradation problems. Recently, numerous deep learning-based methods are proposed for low-light image enhancement. They either focus on performance improvement with negligence of computational complicity, or are extremely computationally efficient networks with poor performance. In this work, we intend to figure out a solution, which strikes a balance between computational cost and performance. Moreover, we observe that different regions of an image contain different amounts of information, where the region with less information is easier to restore than that with more information. Hence, we propose to crop a low-light image into patches and classify these patches into "simple", "medium" and "hard" categories based on their involved information. Then, we enhance different patch categories with different network complexities, therefore, a Category-specific Processing Network (CSPN) is proposed to achieve the computational complexity and performance balance. The patch classification is implemented by the proposed Grey-Level Co-occurrence Matrix (GLCM) entropy-based algorithm, which measures the content complexity of an image by analyzing the statistics of the difference between pixels. As the frequency domain contains exclusive feature information that is beneficial for improving image quality, the wavelet transform is introduced during the enhancement. Extensive experimental results demonstrate the superiority of our proposed CSPN over other state-of-the-art methods in various datasets with the least amount of computational cost.

Backlit image enhancement (BIE) aims to address image degradation caused by challenging lighting conditions. By enhancing the illumination of underexposed areas and restoring image details while avoiding overexposure, it achieves an overall harmonious luminance. In contrast to traditional BIE methods that apply global enhancements with limited effectiveness, we propose a controllable Mamba-based enhancement method, termed Control-Lit. Our method not only delivers effective backlit image enhancement but also allows users to interactively adjust the illumination of specific regions. Control-Lit achieves superior global enhancement performance by employing the Dark Channel Prior (DCP)-based Finite Scalar Quantization (DFSQ) module that provides a high-quality image prior. Additionally, the Dark Channel Prior Enhancement (DCPE) module is designed to guide the network in pixel-wise illumination adjustment at the feature level, thereby achieving enhancement of backlit regions. Furthermore, to address the loss of content and details in backlit regions, the Global-Local Vision State Space (GLVSS) module is incorporated to extract both global and local features for BIE. To enable customizable, controllable enhancement, we introduce an illumination control vector. By adjusting the coefficient map elements that are multiplied by this vector, users can achieve precise regional illumination adjustments. To further validate the generalization of different light enhancement methods, we contribute a synthetic backlit image dataset using a relighting generative model. Along with current widely used datasets, the experimental results demonstrate that our method achieves state-of-the-art performance on all datasets while enabling high-quality and controllable image illumination adjustment. The code is available at https://github.com/wuhj43/Control-Lit.