## 4 Experiments  
### Experimental Setup  
**Dataset:** Publicly available datasets for HDR image reconstruction are currently limited. Previous researchers [38; 39; 40; 42] have utilized various datasets for training and testing, with the majority relying on their own online collections and unpublished datasets. The dataset introduced in the latest NITRE 2021 HDR Challenge [24] is utilized here. The dataset comprises diverse scenes, including outdoor and indoor scenes, daylight and near-light scenes, and complex moving lights, ensuring a high generalization capability for model training. As shown in the documentation provided with the challenge [24], the dataset is a subset of images selected from the HdM HDR dataset [69], where the HDR images were captured by two Alexa Arri cameras with mirrors, and the corresponding LDR images were generated by applying a degradation model (e.g., exposure gain, noise addition, quantization, and clipping) followed by gamma correction. The training set comprises 1494 consecutive frames in 26 long takes, excluding ground truth images for validation and testset. Consequently, adopting a method similar to that of [23], we randomly selected 3 frames in each long take, a total of 78 frames as the testset, and the remaining 1416 frames were utilized for training. When comparing the objective metrics with the latest experimental studies, the models were tested using the HDR-Real testset and the split NTIRE testset. The source code and dataset used in this study are available at [[https://github.com/night333666/RLUNet](https://github.com/night333666/RLUNet)]([https://github.com/night333666/RLUNet](https://github.com/night333666/RLUNet)) (accessed on 30 September 2024).  
**Evaluation Metrics:** The NTIRE dataset is synthetic, while the HDR-Real dataset, introduced by [36], is real-world. Each dataset uses its original evaluation metrics, along with standard HDR assessment metrics. PSNR (Peak Signal-to-Noise Ratio) measures the ratio of the maximum possible pixel value of an image to the pixel error. A high PSNR value typically indicates fewer errors and distortions in brightness and color, as well as better detail recovery. SSIM (Structural Similarity Index Measurement) assesses the similarity between two images in terms of brightness, contrast, and structure. A high SSIM value suggests that the resulting HDR image is structurally closer (e.g., texture, edges, etc.) to the original ground truth image. The NTIRE dataset [24] uses the standard PSNR-L (normalized to the peak of the ground-truth HDR image) to evaluate the output image and the PSNR computed in the u-law tone-mapped image (normalized to 99% of the ground-truth image and limited by the Tanh function to avoid excessive luminance compression). PSNR-L focuses on highlight accuracy, while PSNR-\(\mu\) measures visual similarity. Additionally, HDR-VDP-2 and the SSIM-L calculated using the same normalization, are also included as evaluation metrics. To ensure fairness, Min-Max Normalization is applied to PSNR and SSIM, referred to as PSNR-N and SSIM-N. For the HDR-Real dataset, evaluation includes MSE and HDR-VDP-2 for HDR images, while PSNR-N and SSIM-N, based on Min-Max Normalization, are used for tone-mapped images following [36].  
**Implementation Details:** The training and optimization parameters of the model are detailed in Table 1, which provides comprehensive information on the convolutional kernelsettings, number of channels, and activation functions. The model is developed using the PyTorch framework and trained using NVIDIA GeForce RTX 3090 GPUs.