# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 1 Introduction  
Images captured in low-light or backlit conditions often face challenges, exhibiting low visibility and high noise [1; 2; 3]. These issues pose difficulties for advanced visual requirements and tasks such as image segmentation [4], classification [5], and object detection [6]. As a result, low-light image enhancement (LLIE) strategies aim to improve the visual appeal of low-light images, making them more suitable for use in specific computer vision tasks. Early LLIE techniques predominantly rely on traditional methods, classified into three main categories: histogram-based methods [7; 8; 9], Retinex-based methods [10; 11], and S-curve-based methods [12; 13; 14; 15]. While these methods effectively enhance image visibility, they suffer from drawbacks such as high parameter dependence and poor generalizability. In contrast, supervised deep learning methods, guided by a ground truth, exhibit higher robustness and effectiveness. However, the challenge lies in obtaining the required training data, which heavily relies on low-/normal-light images. Acquiring corresponding paired data is particularly challenging. Synthetic paired data lack authenticity, and acquiring real image pairs involves intricate experimental setups, especially in uncontrolled situations. Moreover, unsupervised deep learning methods have brought about a significant transformation in image enhancement by overcoming the constraints associated with the need for paired data. These methods rely on loss functions designed based on prior knowledge to guide the network, enabling training without a reference image. Despite their promising future, existing unsupervised methods [16; 17; 18; 19; 20] often yield suboptimal visual results when faced with varying light intensities and strong noise in real-life scenes. Theprimary reason is that, in the absence of a ground truth, the deep network struggles to accurately decompose the noise component, limiting its ability to process noise accurately. In addition, the lack of sufficient constraints in the optimization strategy further hinders the recovery of low visibility and low contrast in low-light images. In this paper, we propose an unsupervised method named Self-Guided Pixel-wise Calibration (SGPC) to obtain high-quality enhanced images by integrating the S-curve-based and deep learning networks, effectively addressing the issues mentioned above. We achieve this through three key strategies: Firstly, we introduce a Pixel-wise Guided Filter (PGF) to achieve global adaptive filtering, using a low-light image with relatively low-level noises as a guide and the deep features output from the network as a regularization map. This successfully reduces noise in the enhanced image while preserving texture features in dark areas. Secondly, S-curve-based methods may overlook relationships between neighboring pixels, leaving dull colors undadressed. To tackle this issue, we incorporate a Color Correction Module (CCM) into the deep learning network, enhancing the images and improving their visual appeal. Thirdly, we introduce a pixel-wise exposure control loss (\(\mathcal{L}_{exp}\)), minimizing the distance between the average intensity of a local region in the enhanced image and the well-exposedness map. This can result in a more naturally enhanced image and help reduce local misexposures compared to using a single value globally. Finally, we emphasize the contributions of this work as follows:  
1. An unsupervised low-light image enhancement method is proposed. It effectively addresses a series of challenges arising from the absence of the reference image. Its low data dependence characteristic and lightweight network structure make it suitable for a wider range of application scenarios. 2.