Enhancing Image Encoding with a Convolutional Neural Network Module and Transformer

As is well known, various methods such as convolution operations, feature mapping, and pooling operations can effectively extract rich information from images. Transformer, with its advantages of parallel computation, long-range dependency modeling, and interpretability, has been widely applied in the field of image processing. In order to improve the performance of the encoder, this paper proposes a convolutional neural network module that combines it with the Transformer module. By leveraging the strengths of both, the encoder can effectively capture both local and global features of the input image, thus gaining a more comprehensive understanding of the image content.