## 1 Introduction  
According to the Association of American Railroads (AAR), freight railroads account for approximately 40% of the United States' long-distance freight volume, with over one-third of the nation's exports transported by rail [1]. The U.S. railroad infrastructure predominantly consists of ballasted tracks, where the track substructure plays a crucial role in overall performance. The ballast layer, a key component of the track substructure, provides essential structural support and facilitates drainage, directly influencing track strength, modulus, and deformation behavior. Ballast degrades and breaks down under repeated train loading. This can lead to poor drainage, track settlement, and reduced stability, and may also result in service interruptions and maintenance concerns. The use-related degradation of ballast is commonly referred to as \"fooling\" in railroad engineering practice. Evaluating ballast fouling levels is crucial for railroad inspection and maintenance [2]. The current methods for assessing ballast fouling conditions include visual inspection, field sampling with subsequent laboratory sieve analysis, and the use of Ground Penetrating Radar (GPR) [2]. However, visual inspection can be subjective [3] and field sampling combined with laboratory sieve analysis is labor-intensive. Additionally, GPR data do not offer detailed information on ballast size and shape properties [4; 5]. To address the deficiencies of current ballast evaluation methodologies, computer vision-based techniques have been proposed to enhance the reliability of infrastructure inspections. The advent of Vision Transformer (ViT) architectures [6] has significantly enhanced the accuracy and reliability of ballast particle segmentation from 2D images compared to traditional Convolutional Neural Networks (CNNs). [PERSON] et al. [7] employed a Swin Transformer [8], a hierarchical ViT framework with shifted windows, and Cascade Mask R-CNN [9] and developed a well-tuned model for ballast instance segmentation. Leveraging advanced deep learning techniques, these ballast inspection systems are capable of providing near real-time evaluations in an automated manner, thereby enhancing the analysis of ballast conditions in field environments [7]. Contrary to the segmentation of larger aggregate particles, the task of ballast segmentation is significantly more complex. It necessitates the detection and segmentation of much smaller particles from images obtained under challenging field conditions [7]. This complexity requires higher-quality datasets to train deep learning models, which in turn demands specialized image acquisition devices and hardware for field data collection [10]. Moreover, the annotation process--labeling the contours of ballast particles on images for model training--is both time-consuming and labor-intensive. Consequently, datasets composed of ballast images from field and laboratory sources remain limited in size and are difficult to scale effectively. Data synthesis has been extensively employed to generate training datasets and improve the performance of machine learning (ML) models [11; 12; 13; 14; 15]. Generative Adversarial Networks (GAN) [16] utilize deep learning techniques to avoid complicated modeling and rendering designs and have been used to create synthetic data to assist railroad inspection in practice [17; 18; 19]. These works usually require an input image as a reference and then apply GAN to generate an enhanced image [17; 18] from the poor-quality input or to make the input fake image more similar to a real image. However, GAN models are relatively hard to train and require heavy computational resources, leading to a low-resolution output, typically of size 512 pixel \(\times\) 512 pixel [19], which can severely impact the detection of smaller ballast particles.