Anti-Aliasing for Downsampling in CNNs Based on Gaussian Filter Convolution

Convolutional neural networks leverage their efficient ability to extract common features of images, playing a crucial role in numerous computer vision tasks. Key details such as edges and textures in images often present themselves in the form of high-frequency components, which contain rich semantic information and are essential for accurate image recognition and understanding. However, during the downsampling process, these high-frequency components are improperly mapped to low-frequency components, leading to signal aliasing. This aliasing results in the loss of image detail information and blurred features, significantly affecting the precise extraction of image features by convolutional neural networks and ultimately reducing the performance of the model in various tasks. To effectively address this challenge, this study innovatively proposes the Gaussian Filter Convolution (GFC) module. This module ingeniously utilizes convolution kernels with filtering functions, which can specifically suppress the high-frequency components in the image, reducing the occurrence of signal aliasing at its source, thereby significantly alleviating the aliasing artifacts generated during downsampling. Experimental data show that the model integrated with GFC has significant improvements in key indicators such as model accuracy.