## 2 Related Work
### Deep Learning Methods  
The initial approaches to HDR image reconstruction using deep learning techniques were proposed to overcome the limitations of traditional methods. [PERSON] et al. [10] employed optical flow to align the images relative to the reference image. The authors followed an approach similar to that used in [PERSON] et al. [26], using a convolutional neural network (CNN) to perform the image merging, establishing an initial milestone in the application of deep learning for HDR image reconstruction. As a result, in recent years, various classes of models have been utilized to perform this task.  
GAN-based models have stood out in HDR image reconstruction, both for reconstruction from a single exposure and for reconstruction from multiple exposures. The models that stand out in reconstruction from a single exposure are HDR-cGAN [12] and GlowGAN [13]. HDR-cGAN uses the HDR-REAL and HDR-SYNTH datasets and performs image reconstruction from an architecture based on a conditional GAN [34]; this model uses a pipeline based on three steps, linearization, which estimates the inverse camera function by transferring ldr to the linear domain, overexposure correction, which corrects over/underexposed regions, and refinement, which deals with remaining errors. Meanwhile, GlowGAN combines the structure of a GAN with a stochastic camera model, positioned between the generator and discriminator, enabling training with datasets containing only LDR images. Thus, both approaches treat HDR image reconstruction as an image-to-image translation problem without the need for alignment, generating artifact-free HDR images.  
However, models based on HDR image reconstruction from multiple exposures achieve better results than single-exposure models [3]. For example, the MEF-GAN model [14] has a generator divided into three blocks, including a self-attention block to allow for attention-driven and long-range dependency by introducing the self-attention mechanism, a local detail block to preserve and fuse local details, and a merge block to merge features obtained by the other blocks. The model is trained with the dataset from [PERSON] et al. [35], which includes aligned images from HDR-Eye [25], Fairchild [36], and other images collected by the authors. Additionally, the MEF-GAN model uses minimum and maximum exposure images to generate HDR images, demonstrating excellent results in the exposure merging task. On the other hand, the HDR-GAN [16] and UPHDR-GAN [15] models use three LDR images captured at different exposures with motion to generate HDR images, being trained with the Kalantari dataset [10]. Therefore, they need to perform both alignment and merging. The HDR-GAN model utilizes a multi-scale LDR encoder, reference-aligned feature fusion, and deep HDR supervision to achieve the best results. Meanwhile, the UPHDR-GAN model stands out for incorporating unlabeled data into the training process, increasing its robustness. The model achieves this by using the Min-Patch module during generator training. Both models demonstrate remarkable results for alignment and merging tasks, with a slight superiority of HDR-GAN in performance, resulting in reconstruction with superior quality compared to the other mentioned alternatives.