# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
[PERSON] and [PERSON] [19] explored the application of supervised and unsupervised learning methods in SHM, demonstrating their capability to detect damage locations and severity with high accuracy using sensor data. [PERSON] and [PERSON] [20] reviewed 3D reconstruction techniques in civil engineering, focusing on point cloud models, algorithms for processing them, and applications in construction site and pipeline reconstruction. Currently, numerous studies have addressed the restoration issues of borehole images. [PERSON], [PERSON] et al. [21] investigated an image processing and grayscale correction method for multi-camera borehole imaging devices. [PERSON], HM et al. [22] proposed a probe eccentricity correction method for borehole imaging videos, obtaining corrected borehole videos through video expansion and template matching. [PERSON] et al. [23] introduced an image restoration and enhancement algorithm for imaging robots used in underwater oil and gas pipelines, which reduces blurring effects and enhances image color and contrast. [PERSON] et al. [24] proposed a center point alignment algorithm based on threshold segmentation for forward-looking borehole imaging videos. Advancements in machine learning have addressed challenges related to restoring data in borehole imaging. [PERSON] et al. [25] developed a partial convolutional neural network to fill gaps in borehole images, effectively reconstructing missing data while maintaining textural consistency. [PERSON] et al. [26] explored a rock photo image-enhancement method based on unsupervised deep learning. [PERSON] et al. [27] introduced a restoration scheme for full-borehole electrical imaging, addressing the challenges posed by structural limitations in imaging tools and enabling the effective restoration of missing image segments. [PERSON] et al. [28] reviewed recent advancements in deep learning techniques for image super-resolution, categorizing them into supervised, unsupervised, and domain-specific approaches, and discussed future research directions. For the interpretation of borehole images, [PERSON] et al. [29] utilized digital panoramic borehole cameras to investigate fracturing in high-stress underground environments, demonstrating the capability of imaging tools to capture dynamic geological processes. [PERSON] et al. [30] applied transfer learning techniques to analyze drill core images, achieving a remarkable 96.7% accuracy in predicting mineral compositions and geological formations. While these studies have to some extent improved the imaging quality of borehole images, issues such as the alternating bright and dark bands caused by probe eccentricity in borehole images and the resulting measurement errors in rock mass structure have been rarely addressed. Probe eccentricity during borehole imaging remains a significant challenge, leading to image errors such as alternating bright and dark bands and perspective distortions, which compromise the accuracy of structural parameter measurements. The objective of this study is to address these challenges by developing methods to restore borehole images, thereby correcting the errors and improving the quality and reliability of rock mass structure analysis. To achieve this objective, this study investigates the alternating bright and dark bands characteristic of borehole images, establishes an image grayscale feature model, and develops a three-dimensional positioning analysis method for probes within the borehole. Based on these analyses, grayscale and perspective restoration methods for borehole images are proposed. These methods are applied to typical borehole images from practical engineering projects, and the proposed image restoration approaches are validated and evaluated.