## 4 Deep Learning Methods for HDR Reconstruction
### CNN for HDR Reconstruction
#### 4.1.2 Multi LDR to HDR Reconstruction with CNN  
Multi-frame HDR image reconstruction combines multiple images captured with different exposures to produce a final HDR image. By leveraging a variety of exposures, the model can retain more details in bright and dark regions, which helps achieve results closer to real-world scenarios. More recent methods have explored a blend of techniques such as deformable convolutions [13; 16] and CNNs [117; 118] to address problems like alignment, saturation, and ghosting artifacts. [PERSON] et al. [15] propose using the optical flow algorithm [119] to align LDR images and then fuse them with a CNN for HDR image reconstruction. However, optical flow-based approaches can fail under occlusion or abrupt motion, leading to alternative approaches such as [PERSON] et al. [120], which transformed the problem into an LDR-to-HDR conversion without optical flow using an encoder-decoder architecture for reconstruction.  
Following this concept, ref. [13] introduced the AHDRNet architecture, which is composed of two neural networks for alignment and fusion. The first network is dedicated to extracting features from the input images by means of convolutional encoders, applying attention maps to nonreference images to identify useful features, thereby reducing ghosting artifacts. The configuration can be seen in Figure 6a. Then, the second fusion network uses these attention-guided features to estimate the HDR image using dilated residual dense blocks (DRDBs) and a global residual learning strategy (GRL). Hence, it enhances the use of extracted features, producing HDR images with consistent and realistic detail.  
Figure 5: Single-frame architectures.  
On the other hand, IREANet [121], shown in Figure 5(b), stands out for introducing the Flow-guided Feature Alignment Module (FFAM) and the Enhanced Feature Aggregation Module (EFAM). These components enable more effective alignment and reconstruction of multi-exposure LDR images, mitigating motion artifacts and improving final image quality. Moreover, using the BayerAug (Bayer preserving augmentation) strategy for preserving multi-exposure RAW data broadens the model's generalization, positioning IREANet as one of the leading solutions for HDR reconstruction.  
Lastly, RepUNet [122] made progress by emphasizing efficiency and robustness using structural reparameterization to create a lightweight and fast model for HDR reconstruction, as shown in Figure 7. Additionally, APNT-Fusion [117] proposes an HDR restoration model via progressive neural fusion guided by attention, preventing ghosting artifacts from motion and efficiently transferring texture in saturated regions.  
Figure 6: Multi-frame architectures.  
Figure 7: RepUNet architecture.  
4.1.3. Single and Multi LDR to HDR Reconstruction with [PERSON] et al. [123] proposed an innovative approach for HDR image reconstruction that integrates the two main existing techniques: single-frame and multi-frame. The architecture is predominantly based on the single-frame technique but uses the multi-frame technique in an auxiliary manner, enabling the capture of more detail in overexposed areas and suppressing ghosting artifacts. The model is divided into four modules, among which SHDR-ESI stands out for integrating single-frame with multi-frame to improve final image quality. SHDR-ESI also plays a key role in providing intermediate data to the SHDR-A-MHDR module, which primarily reduces ghosting and enhances overexposed regions. Additionally, the system utilizes DEM, consisting of an SRM (Self-Representation Module) and an MRM (Mutual-Representation Module), to highlight essential information and effectively merge complementary features. The merging of reference and nonreference features is performed using FIFM, ensuring accurate and detailed HDR reconstruction.  
Table 3 presents the metrics-based performance of the aforementioned models.