## 3 Methodology  
### Network Structure of CSNGAN-ASPP  
Figure 3 illustrates the basic framework of 2D and 3D CSNGAN-ASPP. In Figure 3, the generator of CSNGAN-ASPP employs the DCGAN architecture [29], which utilizes the convolutional layer and batch normalization layer instead of the fully  
Figure 3: The basic framework of CSNGAN-ASPP. Figure 2: The architecture of CGAN. connected layer and pooling layer, respectively. By integrating hybrid operator, it can more effectively process feature information at varying scales, thereby ensuring its robust feature extraction and nonlinear fitting capabilities [30]. Additionally, inspired by the DeepLabv3+ network architecture in the field of image segmentation [31], the generator employs an encoder-decoder architecture, which extracts information through the stacking of the convolutional layer and transposed convolutional layer. Specifically, the encoder transmits feature information obtained from the initial down-sampling process into the corresponding position of the decoder to prevent the loss of detail information caused by subsequent multilayer convolution. The deep convolution process can effectively extract the higher-order statistical features of the dataset and pass them into the decoder, guiding the up-sampling reconstruction. The decoder fuses feature information from different scales and gradually recovers the structural attributes of the image through up-sampling operations. While the original DeepLabv3+ network structure is complex and computationally intensive, this paper introduces a lightweight adjustment to the network model to enhance reconstruction efficiency. To ensure that the receptive field effectively extracts feature map information and reduces edge information loss while maintaining constant resolution, this paper introduces dilated convolution. Figure 4 illustrates the schematic diagram of standard and dilated convolution [32]. Dilated convolution increases the effective kernel size by inserting gaps between the convolution kernel elements, while the actual convolutional units involved in the operation remain unchanged. This approach enhances feature extraction by balancing computational efficiency and feature map resolution. Simultaneously, the ASPP structure is incorporated into the encoding part of the generator. ASPP is primarily composed of a standard convolution module, three parallel dilated convolution modules, and a global average pooling module. Among them, the dilation rate \(n\) in the parallel dilated convolutions depends on the size of the input feature maps and represents convolution kernels with varying receptive fields. With different sampling rates, ASPP effectively extracts multi-scale feature information, processing them separately within the corresponding branches. In addition, the global average pooling layer weights and averages the feature values on each channel, which provides the model with greater global awareness and aids in a better understanding of the overall structure and background information of the image. The combination of the convolutional modules and average pooling layer enhances the expressiveness of feature representations. Finally, the feature information extracted from the multiple parallel branches is merged, compressed through additional convolutional layers, and transmitted into the decoding structure. The PatchGAN discriminator structure is incorporated by the discriminator of CSN-GAN-ASPP to enhance the stability of training [33], which primarily consists of convolution layers and activation function layers, effectively mitigating issues of gradient vanishing or exploding. The discriminator outputs a matrix containing multiple scalars, each represent  
Figure 4: Schematic diagram of (**a**) standard convolution and (**b**) dilated convolution. ing the feature information extracted from the corresponding patches. Connections exist between neighboring patches, forming an approximation of a Markov random field.