## 1 Introduction  
Computed tomography (CT) is an essential technique to obtain a three-dimensional (3D) representation of a scanned object in medical imaging, industrial imaging, and other areas. In medical imaging, CT can determine injuries or diseases in nearly all parts of the body. In industrial imaging, CT can be used for nondestructive testing to detect cracks or defects. Irradiating a target object with a sufficient radiation dose is necessary to obtain a good quality 3D image. However, in real-world situations, a target object cannot be sufficiently irradiated due to hardware limitations or public healthcare issues. Especially in medical imaging, the radiation in CT can cause cancer and genetic or other diseases [1]. Thus, it is crucial to minimize and optimize the radiation dose in CT. In addition, CT images are reconstructed from a plurality of the projections acquired from an X-ray detector as the X-ray tube and detector rotate through the target object. The radiation dose reduction causes severe photon noise with a Poisson distribution [2] in the projection images, degrading the quality of the 3D reconstructed image. For example, photon noise in projection images is a source of streak artifacts in reconstructed images. Numerous studies in low-dose CT (LDCT) have been proposed using either conventional image processing-based denoising methods [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] or deep learning-based denoising methods [17; 18; 19; 20; 21; 22]. Conventional image processing-based denoising methods show high performance. However, there are some problems, such as a removed microstructure, blurred edges, or high computational complexity. To solve these problems, deep learning-based approaches are proposed. These methods have exhibited superior performance. However, there are still problems, namely that it is difficult to obtain appropriate datasets due to public health care issues (the details are described in Section 2). Therefore, the need for deep learning-based denoising methods using self-supervised or unsupervised training schemes has emerged. Even in noise reduction in the natural image domain, it is challenging to obtain paired noisy and clean images for training a supervised denoising network. Several studies that train networks without paired noisy and clean images in the natural image domain have been proposed to handle this problem. The Noise2 Noise [23] method has proved that denoising networks can be trained using paired noisy and noisy images in specific environments. However, even if the Noise2 Noise approach can train networks without paired noisy and clean images, the method still requires paired noisy and noisy images with independent noise. Based on the Noise2 Noise theory, several studies that can denoise an image without paired images have been proposed to address this problem. Noise2 Void (\(N2V\)) [24] introduces the blind spot strategy with self-prediction loss to train networks without paired images. The blind spot strategy allows for effectively predicting artificially missing pixels using the neighboring noisy pixels. Noise2 Self (\(N2S\)) [25] also demonstrates training neural networks using self-supervision for noise reduction in the natural image domain. Motivated by these studies, the Self2 Self [26] approach proposed a method of training networks with pairs of Bernoulli-sampled input images from a single image. The Self2 Self approach uses the method with dropout of both the input image and convolutional layers in the decoding part and the method of estimating noise-reduced results by averaging predictions generated from multiple outputs of the trained model. These methods allow for lowering the variance of the denoising neural network, which can be interpreted as a Bayes estimator. The dropout-based ensemble method is very efficient and demonstrates superior performance in noise reduction without paired images.