## 1 Introduction  
With the advent of science and technology, the application of transparent images has increasingly been used in various fields around humans, such as the segmentation of renal transparent cancer cell nuclei in medicine [1]. The shape and location information of the cell nucleus are of great significance in the segmentation and diagnosis of benign and malignant tumors [2, 3]. Another example is the identification of the number of transparent microorganisms in an environment to determine the degree of environmental pollution [4]. In recent years, the segmentation of transparent objects in images has become a hot topic in vision research [5; 6]. It is not easy to detect whether there are transparent or translucent objects in the images because the transparent object area to be observed is generally small or thin, and the colors and contrasts of the foreground and background are similar [7]. Only the residual edge can lead to a low resolution of the foreground or background, which largely depends on the background and lighting conditions. Therefore, there is an urgent need for effective methods for identifying transparent or translucent images. In recent years, deep learning has achieved a good performance in the field of computer vision. We consider the excellent performance of computer vision in image analysis [8], such as its high speed, high accuracy, low consumption, high degree of quantification, and strong objectivity [9]. Additionally, compared with traditional manual methods, computer image analysis can reduce manual effort from time-consuming tasks. Consequently, computer vision-based approaches can overcome the limitations of traditional methods and demonstrate great potential for transparent image analyses [10]. Particularly, when the object is transparent or has low contrast in the image, more foreground information is required; therefore, more visual details are found to recover the lost information from patches or pixels. As shown in Figure 1, the foreground and background of microorganisms are similar. There is only a small amount of information on the edges; which makes it difficult for traditional convolutional neural network (CNN) algorithms to distinguish transparent objects in images globally [11]. To address this problem, it is necessary to analyze transparent images from patches. We cropped the image into fixed-size patches and created a deep learning network to learn the features of the visual information of foreground and background patches. The network trained in this manner is sensitive to the foreground and background, which helps to distinguish transparent objects and achieve segmentation. In recent years, machine vision has been widely used for image processing [12; 13]. Deep learning is a more effective method in the field of machine vision, such as the popular _Convolutional Neural Network_ (CNN) Xception, VGG-16, Resnet50, Inception-V3, U-Net, and novel _Visual Transformers_ (ViT) [14]. CNNs gradually expand the receptive field by increasing the size of the convolution kernel until it covers the entire image; thus, CNNs complete the image extraction from local to global information. Contrarily, transformers can obtain global information from the beginning, making learning more challenging, but their ability to retain long-term dependence is more potent than that of CNN [15]. Therefore, CNNs and transformers have both advantages and disadvantages when handling visual information. Therefore, this study compares the patch-level and pixel-level segmentation performance of transparent images with different CNN and visual transformer methods. This study aims to determine the adaptability of various deep learning models in this research domain. The main contributions of this paper are as follows:  
(1) A comparative study on patch-level transparent image segmentation was conducted to help analyze transparent images. Figure 1: An example of transparent images (a low contrast environmental microorganism image).