# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 2 Related Work  
### Low-Light Image Enhancement  
In traditional LIE approaches, methods like histogram equalization and gamma correction [15] directly enhance the visibility and contrast of low-light images. However, these techniques often overlook the illumination intensity, which makes those processed images deviate from the real-world lighting conditions. With the rapid development of deep learning, Convolutional Neural Networks (CNNs) have been widely applied to the LIE area with the sRGB domain. For instance, [PERSON] et al. [16] propose a method that employs global curves for brightness adjustment. Additionally, [PERSON] et al. [17] propose MIR-Net to integrate multi-scale contextual information while preserving high-resolution spatial details. [PERSON] et al. introduces network DeepUPE [18], an advanced neural network for photo enhancement that incorporates intermediate illumination layers which leads to the recovery of clear details. [PERSON] et al. [19] introduce LightDiff to enhance the quality of low-light images for autonomous driving applications. Despite the advancements, these CNN-based methods face big challenges in capturing long-distance connections between image pixels. To address this issue, the network Restormer [20] is proposed to incorporate a Transformer structure into the U-Net architecture to improve global connections between features. [PERSON] et al. propose an innovative one-stage framework [9] for low-light image enhancement, named Retinexformer, which integrates the strengths of Retinex theory with the Transformer architecture. Although this novel framework exhibits great performance across  
Figure 1: Experimental results on four datasets. This is a comprehensive comparison chart of results in both sRGB and raw domains, with detailed data available in Section 4. The horizontal and vertical axes of the chart represent PSNR and SSIM, respectively. The better the performance, the further right and up the model is on the chart. It can be seen that our model achieves excellent performance.  
various datasets, its computational complexity is high, particularly for high-resolution images. For structural light weighting, [PERSON] et al. [21] introduce a structure-aware lightweight Transformer for image enhancement tasks. [PERSON] et al. [1] present a compact model with only 90k parameters, incorporating a global attention module to simulate ISP processes for LIE.  
However, these deep learning models are primarily designed for sRGB images, which creates a domain gap when applied to raw images and reduces the models' generalization performance. Therefore, [PERSON] et al. [22] proposed REENet which converts sRGB images to the linear raw domain when enhancing the images. Recently, in the raw domain, [PERSON] et al. [4] proposed SNR, which dynamically adapts long-range operations for noisy areas and short-range operations for clearer regions, enhancing the overall image quality effectively. However, the reliance on the SNR prior may affect the accuracy of enhancement across different images. [PERSON] et al. [23] introduces a raw domain low-light image dataset and directly enhance the raw data based on end-to-end deep learning pipeline. Nonetheless, methods leveraging raw images for LIE generally require raw images as training data. This poses challenges, as raw images are often difficult to obtain and inconvenient for training. To address these issues, our study introduces a novel model that leverages sRGB-raw-sRGB transformation before LIE. This approach allows the model to operate effectively in both raw and sRGB domains, mitigating the limitations associated with raw image acquisition while achieving high performance across both domains.