# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 1 Introduction  
Low-light image enhancement (LIE) is a crucial task in the field of computer vision (CV). LIE aims to improve the contrast of images captured under low-light conditions. By extracting more detailed information from the enhanced images, it can increase the accuracy of many downstream visual tasks, such as image segmentation and object detection [1]. Recently, with the rapid development of deep learning and the increasing availability of training data, many efficient LIE models, based on deep neural networks (DNNs), have been proposed [2, 3, 4]. These models have demonstrated excellent performance on many CV tasks. Among these models, researchers have designed various modules to solve different challenges in LIE, for instance, generative adversarial networks [5, 6], diffusion models [7, 8], and attention mechanisms [9]. However, these low-light image enhancement models are mostly suitable for standard RGB images (sRGB). This preference is due to sRGB's convenient storage and smaller filesize. In contrast, pixel values in raw images have a linear relationship with the scene's radiance, which typically have higher bit depth, offering richer image information [10]. Additionally, degradation in actual images mainly occurs in the raw domain, so enhancing images based on the raw format has the advantage of effectively restoring shadows and highlights without introducing noise associated with high ISO levels [11]. Consequently, there has been a growing interest in performing image enhancement directly in the raw domain. For instance, [PERSON] et al. [12] uses a demosaicing module with a Gated Feedback Module and Frequency Selection Module for image demoireing. [PERSON] et al. [13] introduces the BSRaw model to address sensor noise in super-resolution. Recently, for LIE tasks, there are many researchers using raw images as input; this can more accurately restore the true brightness and color information and further improve the effectiveness and precision of these tasks [1,14]. While raw images offer superior results in LIE, as seen in [12], the widespread use of sRGB images in real-world applications still cannot be ignored. Consequently, integrating the advantages of both raw and sRGB images has, therefore, become a key direction for LIE. However, original raw images often present challenges such as limited accessibility, larger file sizes, and complex data structures, making them difficult to use directly in many applications. This necessitates developing techniques to reconstruct raw images from sRGB inputs, thereby leveraging the benefits of raw image processing without directly relying on raw image training data. This process, however, is non-trivial due to the substantial differences between sRGB and raw formats. A key challenge lies in designing an intermediate RGB feature map to facilitate smoother raw reconstruction while establishing effective global attention to link sRGB and raw pixel relationships. To address these issues, we propose a novel method, sRrsR-Net, which efficiently integrates the advantages of reconstructed raw and sRGB images through a three-stage framework: **sRGB-raw-sRGB**. This model is designed to enhance low-light images while avoiding the direct use of raw images as training data. The main contributions of this work are three innovative modules, which are the RGB-iRGB, RAWFormer, and RRAW-sRGB modules. Firstly, the RGB-iRGB module converts sRGB to an intermediate RGB feature map, enabling smoother raw reconstruction. This module uses metadata to make full use of the rich details in the raw image and improve the enhancement effectiveness. Secondly, during the raw reconstruction process, there is a significant difference in the pixel distribution between raw and sRGB images. To establish global attention among sRGB and raw pixels, the RAWFormer module is proposed. It employs a global attention mechanism to transform the low-light input image from R-G-B to the R-G-G-B Bayer format.