# sRrsR-Net: A New Low-Light Image Enhancement Network via Raw Image Reconstruction
## 4 Experiments  
### Datasets, Metrics, and Implementation Details  
**Datasets**. In this paper, the experiments were conducted on four public datasets to assess the performance of our low-light image enhancement model sRrsR-Net. They are LOL-v1 [35], LOL-v2 [36], VE-LOL [37], and SID [23]. Among them, the former three belong to sRGB domains, and the last belongs to the raw domain. Specially, the LOL-v2 dataset is further divided into real and synthetic subsets, named LOL-v2-real and LOL-v2-synthetic. All these datasets consist of pairs of low-light and normal-light images. The distribution of training and test sets for LOL-v1, LOL-v2-real, LOL-v2- synthetic, VE-LOL, and SID are the ratios of 485:15, 689:100, 900:100, 800:200, and 800:200, respectively. For the raw dataset SID [23], there are 2688 pairs of short-long exposure raw images captured by Sony cameras.  
Each dataset presents unique challenges and features that influence the evaluation of our model. The LOL-v1 dataset offers a starting point for research in real-world low-light conditions but may lack the diversity needed for robust model training due to its relatively small size. The LOL-v2-real subset expands upon LOL-v1's foundation by providing a larger dataset (789 pairs).  
**Metrics**. Here, three metrics for the quantitative evaluation of sRsrR-Net model performance are selected, they are the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) [38], and Learned Perceptual Image Patch Similarity (LPIPS) [39]. Both PSNR and SSIM are objective metrics, and LPIPS can better conform to human visual perception. Specially, the PSNR measures the maximum signal power-to-noise power ratio, primarily focusing on global quality aspects such as brightness and contrast; the SSIM mainly delves into the visual impact of image similarity by considering luminance, contrast, and structural information, offering a more perceptually aligned evaluation; and the LPIPS is a deep learning-based metric that is capable of capturing the subtle perceptual differences in image quality that the human visual system can discern.  
**Implementation Details**. In our experiments, the model sRrsR-Net was implemented by PyTorch and trained on a single NVIDIA A100 GPU with a batch size of 32 for 120 epochs. The Adam optimizer was employed for parameter tuning, with an initial learning rate of 1 \(\times\) 10\({}^{-4}\) and weight decay set to 0. During the training phase, the low-light images for input were resized to 224 \(\times\) 224, and they underwent flipping and rotation as part of the data augmentation process. In addition, other comparative models also applied the same image preprocessing methods as the sRsrR-Net model. In the following experiments, many low-light image enhancement approaches were considered, such as Zero-DCE, DeepUPE, EnGAN, UFormer, MIRNet, Sparse, Retinexformer, RQ, SNR, SID, EEMEFN, FIDE, LLNet, RRDNet, EnlightenGAN, KinD, KinD++, RUAS, LLFlow, and so on.  
Firstly, to evaluate these models, experiments were conducted on both the sRGB datasets (LOL-v1, LOL-v2-real, and LOL-v2-synthetic) and the raw domain dataset (SID), aiming to enhance low-light conditions in various scenarios. Secondly, for generalization performance, several models were trained on the LOL-v1 dataset, and then they were tested on the VE-LOL dataset. In terms of computational performance, comparisons were made among different model architectures, specifically those based on Transformer, CNN, and U-Net frameworks. Moreover, a lot of ablation experiments were performed to validate the effectiveness of three modules (i.e., RGB-iRGB, RAWFormer, and RRAW-sRGB) in the model sRsrR-Net.