## 1 Introduction  
Optical remote sensing systems can obtain observations of the ocean, the atmosphere and the Earth's surface. The images available for remote sensing are mostly observed from a distance and are affected by atmospheric conditions and climatic factors, such as clouds [1]. Cloud cover alters the useful information in images, which is an adversity for subsequent image interpretation, as it can weaken or even cause the loss of essential information for analysis [2]. According to [3], depending on the application, the removal task is somewhat challenging, and there are currently some efforts to address this problem. In accordance with the data source, image-based methods can be divided into two categories: methods based on multiple images and methods based on a single image. When multiple images are used, the methods correct the brightness of cloudy pixels by fusing complementary information from other temporal images or other sensors. On the other hand, methods based on a single image are independent of the referenced data. Among the efforts reported in the literature, the problem is treated with four types of methods: spectral-based, spatial-based, temporal-based and hybrid methods. In [4],a spectral-based method is presented for removing thin clouds in Sentinel-2A images, instruments of the European Space Agency's (ESA's) Copernicus mission, in which a semi-supervised method called CR-GAN-PM is proposed; the method combines generative adversarial networks (GANs) and a physical model of cloud distortion. Similarly, ref. [2] uses GANs to remove clouds from Sentinel images captured on the Google Earth platform, employing spatial-based methods. In [5], they apply spatial-based methods using convolutional neural networks (CNNs); the network is structured in two stages, one to extract cloud transparency information and the other to retrieve information from the ground surface. The authors use the L7 Irish and LSSPARCS datasets from Sentinel-2 to perform the cloud-removal task. In [6], temporal-based methods are used to remove clouds in radar images from Sentinel-1 and 2. The authors create the SEN12 MS-CR-TS dataset and employ it to train a network capable of reconstructing cloud-covered pixels. They also present a second approach that introduces a neural network that predicts data compromised by clouds using a time series. In [7], hybrid methods are applied employing a flexible form of spatial-based methods with deep learning structures based on GANs, allowing for the use of three arbitrary temporal images as references for the removal of dense clouds in Landsat-8 OLI and Sentinel-2 MSI images. The authors use FMask-based algorithms for cloud detection. Some cloud removal processes use segmentation algorithms for cloud detection, such as the C Function of Mask (CFMask) algorithm [8], which is an algorithm intended to work with Landsat 7 and 8 images and which stands out for its efficiency in detecting cloud, thin cloud, clear, and some cloud shadows. The Image Inpainting, approach aims to reconstruct images by removing unwanted information, incorporating missing information or presenting them in an imperceptible way [9]. According to [10], inpainting is a constantly evolving approach in the field of image processing capable of reconstructing images with greater efficiency in terms of the time spent on the process and the computational cost and has shown great promises in the treatment of damaged images. Although initially inpainting techniques were based on partial differential equations, their implementation with deep learning algorithms has shown significantly better results, which has led to the development of numerous approaches for image reconstruction, generation and compression [11].