# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 1 Introduction  
High-quality images are crucial for computer vision tasks, but those captured under low light, backlight, uneven lighting, and noise conditions often suffer from low contrast, high noise, information loss in dark areas, and blurred details. These issues can lead to the failure of tasks like object detection and semantic segmentation, making low-light image enhancement (LLIE) vital for improving computer vision performance. Traditional low-light image enhancement (LLIE) methods encompass histogram equalization-based techniques [1, 2, 3] and Retinex model-based approaches [4, 5, 6, 7]. Histogram-based methods adjust the pixel intensities to improve contrast and brightness, but often struggle to balance dark and bright areas and are not well suited to varying lighting conditions. Retinex models, which decompose images into reflectance and illumination [8], have led to various methods such as MSRCR [9], LIME [10], and Robust Retinex [11]. However, they frequently assume that reflectance is sufficient for enhancement, which can result in color distortion and loss of detail under different lighting. End-to-end learning approaches like EFINet [12], DCMAN [13], as well as other specialized networks like TreEnhance [14], have enhanced LLIE performance. Deep learning methods based on Retinex theory incorporate noise reduction by separating images into reflectance andillumination, using progressive enhancement to stabilize outputs, and integrating semantic information to refine component estimation [5; 6; 7; 15]. However, Retinex-based approaches have limitations, such as assuming constant reflectance under varying lighting, leading to artifacts or color distortions. They struggle with extreme low-light conditions with high noise and uneven illumination, causing detail loss and inadequate enhancement [16; 17; 18]. Additionally, their reliance on handcrafted priors limits flexibility and adaptability to diverse low-light scenarios [19; 20; 21]. Denoising diffusion probabilistic models (DDPMs) have recently achieved ground-breaking advancements in image generation [22], making them increasingly influential in various visual tasks, including image generation, restoration, super-resolution, and denoising [23; 24]. Recently, researchers have applied diffusion models to LLIE, achieving state-of-the-art results [25; 26; 27]. These models utilize a diffusion process that converts a clean image into a noisy version during training and then reverses the Markov chain to produce new images during testing. Building on this success, [PERSON] et al. [27] proposed PyramidDiffusion (PyDiff), a model that accelerates low-light image enhancement and simplifies training while maintaining performance using pyramid diffusion and a global corrector. Then, [PERSON] et al. [26] designed a novel method for image restoration using a statistic-based entropy loss with diffusion models to improve the accuracy and perceptual quality over the traditional \(\ell_{1}\) loss. In addition, [PERSON] et al. proposed DiffLL [25], a robust and efficient diffusion-based low-light image enhancement method using a wavelet-based conditional diffusion model (WCDM) to improve perceptual fidelity and computational efficiency. Recent studies have introduced innovative methodologies to improve the accuracy and computational efficiency of LLIE. Nevertheless, although the aforementioned approaches primarily concentrate on tailoring the diffusion model structure to suit the LLIE task, they fail to delve deeper into the global feature extraction and precise guidance of network training, which are crucial for effective LLIE. Furthermore, [PERSON] et al. proposed a diffusion-based framework for low-light image enhancement [28], incorporating global structure-aware stable optimization regularization and uncertainty-guided techniques to improve detail preservation, noise suppression, and contrast enhancement.