"应用数学专业下的脉冲耦合神经网络在图像处理中的研究"

The research paper titled "An Applied Research of Pulse Coupled Neural Network for Image Processing" by Hu Junmei explores the application of Pulse Coupled Neural Network (PCNN) in image processing. The study is conducted under the supervision of Cao Genniu and Feng Weibing in the field of Applied Mathematics. PCNN is a novel type of neural network that mimics the human brain's synchronous firing of neurons. In this research, a simplified version of PCNN is enhanced and utilized for image processing. The study primarily focuses on two main aspects: Firstly, the improved PCNN model is employed for image segmentation. Image segmentation is the process of partitioning an image into multiple segments to simplify the representation of the image and make it easier to analyze. The PCNN model shows promising results in segmenting images accurately and efficiently, compared to traditional methods. Secondly, the enhanced PCNN model is utilized for image fusion. Image fusion is the process of combining multiple images to generate a single composite image that contains the relevant information from each of the input images. The PCNN model is found to be effective in fusing images while preserving important features and enhancing the overall visual quality of the fused image. Overall, the research demonstrates the potential of PCNN in image processing tasks such as segmentation and fusion. The improved PCNN model shows advantages in terms of accuracy, efficiency, and visual quality, making it a valuable tool for various image processing applications. The study contributes to the growing field of artificial intelligence and image processing by introducing an innovative approach that shows promising results in enhancing image analysis and manipulation.