to perform alignment with a reference image. Additionally, with the implementation of deep learning techniques, methods for HDR reconstruction from a single exposure have emerged, eliminating the need for image alignment or fusion [3; 6]. However, traditional HDR image reconstruction methods such as those of [PERSON] et al. [7] have encountered challenges in generating high-quality HDR images when there is movement in the scene, due to occlusion of certain areas and misalignment between the images. In Figure 1, the reconstruction performed using this traditional method from three inputs with motion is illustrated [7]. It becomes possible to observe that traditional methods can generate images that exhibit artifacts such as ghosts and blurs, which are characterized as the partial appearance of an element that was in motion, either due to the camera or the time interval between the input images. Ghosts are related to large movements, while blurs are related to minor movements [8; 9]. As a way to solve the problems of image fusion, several studies have implemented the use of deep learning techniques, both for the image alignment problem and for the generation of missing information. Among these techniques, those based on Generative Adversarial Networks (GANs) [11] stand out, where architectures such as HDR-cGAN [12] and GlowGan [13] were developed for HDR image reconstruction from a single exposure, and MEF-GAN [14], UPHDR-GAN [15], and HDR-GAN [16] for HDR image reconstruction from multiple exposures, demonstrating excellent results. In Figure 2, the reconstruction of a sample using HDR-GAN is presented. GANs consist of a generator model that produces samples and a discriminator model that evaluates and determines whether these samples are real or generated. During training, these models compete with each other, and this dynamic results in an enhanced ability to create realistic data. Due to this, GANs are widely used in applications such as image synthesis and visual restoration [17]. Figure 1: Reconstruction of image 010 from [PERSON] et al.’s dataset [10] using [PERSON] et al.’s [7] method. Excellent performance is provided for HDR reconstruction through the implementation of deep learning techniques, which is why many studies have investigated the use of these techniques to reduce the computational cost required for their execution [18]. Among the alternatives developed to improve computational cost, depthwise separable convolution [19] stands out. It replaces standard convolution by separating spatial-wise and channel-wise computation steps, emerging as a solution to optimize neural networks by reducing computational costs and the number of parameters compared to traditional convolutions. Since then, the use of depthwise separable convolution in the development of neural networks has been applied in various architectures, such as MobileNet [20] for object detection, SynthNet [21] for image synthesis, SwiftSRGAN [22] for super-resolution, [PERSON] et al. [23] for fabric defect detection, and [PERSON] et al. [24] for stereo-matching, among others. These architectures have demonstrated the efficiency of the approach in developing lightweight models while maintaining competitive performance. In this context, this paper presents an optimized version of the HDR-GAN architecture [16], a well-established and purely convolutional model, in which it is proposed to replace standard convolution with depthwise separable convolution. Although depthwise separable convolution has already been used in other models, this research is the first to specifically optimize an existing HDR reconstruction model to evaluate the efficiency and computational cost reduction of this modification. This innovative approach aims to significantly reduce the computational cost of the HDR image reconstruction process from multiple exposures with motion, offering a unique contribution by demonstrating the practical benefits of this optimization on an already established model.