## 3 Datasets and Evaluation Metrics
### Datasets
#### 3.1.1 Real Multi-Frame HDR Datasets  
The dataset by [PERSON] et al. [15] has 89 sequences of real LDR images captured in dynamic scenes with their corresponding HDR bracketing images. The images, with resolutions of 1500 \(\times\) 1000 pixels, were collected in multiple exposures, which makes this dataset ideal for evaluating HDR reconstruction algorithms, especially in scenarios involving movement, such as deghosting. This set is widely used as one of the main references in work involving machine learning applied to HDR. Similarly, the dataset by [PERSON] et al. [65] contains eight sequences of LDR images extracted from real HDR videos, with a resolution of 1350 \(\times\) 900 pixels. This dataset stands out for capturing continuous movement in multiple frames, which makes it especially useful for developing algorithms aimed at reconstructing HDR in videos, as well as deghosting. Despite its low number of samples, the complexity of the dynamic scenes makes it a popular choice for HDR reconstruction benchmarks. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Quantity** & **Resolution** & **Type** & **Key Features** & **Applications** \\ \hline IISc VAL 7-1 [76] & 84 sequences & 1000 \(\times\) 1500 & Real & Dynamic Scenes, Reconstruction Method Evaluation & Deghosting, Multi-Frame Fusion \\ \hline [PERSON] et al. [77] & 105 scenes & 2142 \(\times\) 1422 & Real & Multiple Exposures, Broad Range of Scenarios, Deghosting & HDR Reconstruction, Deghosting Studies \\ \hline [PERSON] et al. [78] & 16 sequences & 1024 \(\times\) 682 & Real & Indoor and Outdoor Scenes, Motion Focus & HDR Fusion, Deghosting \\ \hline HDR-Eye [79] & 46 images & 1920 \(\times\) 1080 & Real & Multi-Frame Images of Natural Scenes, Humans, Stained Glass, Sculptures & Assessment of HDR Algorithms in Complex Scenarios \\ \hline Laval Indoor [80] & 2100 images & 2048 \(\times\) 1024 & Real & High-Resolution Indoor Panoramas, Multi-Exposure & HDR Reconstruction in Outdoor Environments \\ \hline Laval Outdoor [80] & 205 images & 2048 \(\times\) 1024 & Real & High-Resolution Outdoor Panoramas, Multi-Exposure & HDR Reconstruction in Outdoor Environments \\ \hline [PERSON] et al. [40] & 502 sequences & 512 \(\times\) 512 & Real/ Synthetic & Indoor and Outdoor Scenes, Reconstruction from a Single LDR Image & Single-Frame HDR Reconstruction \\ \hline [PERSON] et al. [81] & 1352 sequences & 4K & Synthetic & LDR to HDR Conversion in Videos, Dynamic and Static Scenarios & HDR Video Compression and Quality Optimization \\ \hline SI-HDR [82] & 181 images & 1920 \(\times\) 1280 & Synthetic & Simulation of Different Exposures Using CRF, Multiple Frames & Multi-Frame Fusion, Full Dynamic Range Capture \\ \hline NTIRE2021 [83] & 1761 images & 1900 \(\times\) 1060 & Real HDR/ Synthetic LDR & Multi- and Single-Frame, 29 Scenes & HDR Reconstruction from LDR, NTIRE 2021 Challenge \\ \hline DeepHDRVideo [31] & Real Videos & 1080p & Real & Complex Scenes, HDR Algorithm Evaluation in Videos & HDR Video Reconstruction \\ \hline GTA-HDR [84] & 40,000 images & 512 \(\times\) 512 & Synthetic & Scenes from GTA V Game, Variety of Lighting and Dynamic Environments & HDR Reconstruction Techniques in Synthetic Environments \\ \hline [PERSON] et al. [85] & 880 scenes & 2304 \(\times\) 1728 & Real & Medical Images, Significant Lighting Variations & Advanced Image Processing, HDR Reconstruction \\ \hline Google HDR+ [52] & 3640 sequences & Various & Real & Burst Mode Images, Multiple Exposures, Less Noise & Mobile HDR Processing \\ \hline Mobile HDR [86] & 251 sequences & 4K & Real & Captured by Smartphones, Wide Range of Lighting Conditions & Mobile HDR Reconstruction \\ \hline UPIQ [87] & 3779 LDR/380 HDR images & Various & Real & Absolute Photometric and Colorimetric Units, Derived from Known IQA Datasets & HDR and LDR Image Quality Evaluation \\ \hline \hline \end{tabular}
\end{table}