# An Improved GAN-Based Image Restoration Method for Imaging Logging Images
###### Abstract
The traditional exemplar-based method PatchMatch [3], which fills the gaps by iteratively searching for the most appropriate patch, is still limited in capturing high-level semantics and may not be able to generate complex and non-repetitive structures. [PERSON] et al. [4] started from image texture and image content, and improved the restoration of the boundary area to be restored by fusing the two with a multi-scale feature extraction module for joint optimisation, while with model training, it would automatically match and adapt the features extracted by the deep network to generate high-frequency details. However, the method was not effective for stratigraphic structures with wide gaps, and for complex structures, such as sand and gravel strata and carbonate formations, the filled area is blurred. [PERSON] et al. [5] used a multi-point geostatistics-based Filtersim algorithm to fill the gaps, but the continuity of the filled layers is not strong enough to obtain a better filling effect. [PERSON] et al. [6] considered convolutional neural networks. CNN supervised learning has been widely used in computer vision applications. In contrast, unsupervised learning based on CNNs has received less attention [7]. Therefore, [PERSON] et al. proposed a class of deep learning models called deep convolutional generative adversarial networks with certain structural constraints and a powerful algorithmic model for unsupervised learning, which was tested with good results from different perspectives on the model networks. [PERSON] et al. [8] proposed a foreground-aware image restoration model that explicitly separated structural inference and content filling, and although the model predicted reasonable contours of objects, restoration in terms of texture details was slightly inadequate. However, most of the existing CNN- and U-Net-based methods usually employ standard convolution, which makes it difficult to process valid pixels and missing parts differently. As a result, they are limited in dealing with large areas of missing images and are more likely to produce colour discrepancies and blurred restoration results. As a remedy, several post-processing techniques have been introduced, but they are still not sufficient to solve the problem. [PERSON] et al. [9] combined a generative adversarial network and a twin neural network as a discriminator in GAN and used the mean square error in the generative network and the contrast loss in the twin network, but it is slightly insufficient in image detail restoration. In order to fill the missing parts in imaging logging images more effectively in practical applications, we propose an innovative image restoration method based on generative adversarial networks (GANs). In the sample preprocessing stage, we inject image annotation information to generate masked images, and through continuous data training, we make the generative network and discriminative network collaborate with each other so as to better learn the image features and reach an optimised model state. With end-to-end training, our method can effectively cope with the lack of contextual connectivity and propagation. Taking the imaging logging data of a horizontal well operation area in Daqing Oilfield as an example, we successfully use the improved GAN-based method to repair the imaging logging images of known horizontal wells and fill in the originally missing regions, demonstrating the application potential of the method in practical applications.