Their strength lies in the precise modeling of complex distributions and the iterative refinement of the output, making them highly effective in generating coherent and high-quality images, with potential for reducing artifacts in tasks like HDR reconstruction [19; 28]. In the field of HDR video generation, although it is one of the most promising applications, there is relatively less dedicated research due to, among other factors, the inherent challenges of temporal inconsistencies and artifacts arising from the fusion of consecutive frames with alternating exposures [29]. Even so, there are methods that achieve results comparable to those of static image reconstruction, including approaches that employ frame alignment via optical flow [30; 31; 32] and attention blocks [33; 34]. For the proper development of these models, robust and high-quality datasets are a resource of great importance, making them fundamental for advancing research in HDR image reconstruction. Such datasets provide a solid basis for training and evaluating algorithms, allowing new techniques to be consistently compared and improved [9]. Other important characteristics that will be analyzed in this review include the type of capture (real or synthetic), resolution, number of images, and specific applications, which bring valuable contributions to the development of reconstruction methods for both single-frame and multi-frame images, as well as covering different scenarios, including mobile devices and videos. Finally, evaluating the quality of HDR images requires the use of specific metrics that take into account the unique characteristics of this type of image. Traditional LDR metrics are not directly applicable to HDR due to differences in dynamic range and visual perception. For this reason, specialized metrics have been created, such as HDR-VDP [35] and HDR-VDP [36], which allow for a more rigorous analysis. Additionally, LDR metrics can be used to evaluate an HDR image, provided that linearization methods, like PU21 [37] or tone mapping, are used, adapting the image to these metrics. A crucial aspect, often underdiscussed and which this review aims to address, is how well these metrics correlate with subjective human judgments of image quality, as they are based on qualitative analyses with individuals. In previous research, such as that of [29], a comprehensive review of HDR reconstruction was presented, with its scope limited to a taxonomy of classical techniques based on CNNs and GANs. Furthermore, we also acknowledge the contribution of the paper by [38], which offered an extensive review of HDR Image Quality Assessment (IQA), focusing on subjective and objective quality assessment metrics. Building upon this research, this paper advances this landscape by incorporating emerging architectures, particularly Transformers and Diffusion Models, and by examining how they address key challenges such as explicit versus implicit alignment and artifact reduction. We comparatively discuss recent methods, such as PASTA, DCDR-UNet, DiffHDR, and IREANet, detailing the specific strategies each employs to address issues of saturation, noise, and motion in dynamic scenes. Thus, our review complements the existing literature by compiling and analyzing both specific HDR metrics (e.g., HDR-VDP-3) and robust adaptations of LDR metrics (PU21-SSIM, PQ-FSIM, among others), discussing their relevance for the validation of reconstruction algorithms. In summary, this review seeks to provide a distinct analysis of the HDR reconstruction field by synthesizing the rapid advances driven by emerging architectures (ViT, DMs) and established ones (CNN, GAN) and their effectiveness in addressing specific HDR challenges, such as alignment, artifacts, and dynamic range expansion. Additionally, the review examines evaluation methodologies, including the perceptual relevance of metrics and datasets.