基于人工智能的图像识别算法研究及优化
近年来，随着人工智能的快速发展，图像识别技术得到了广泛的应用，但是在实际的应用中，还存在着一些数据噪声、图像失真等问题，这些问题直接影响了图像识别算法的准确性和效率。因此，在本篇论文中，我们将对基于人工智能的图像识别算法进行深入研究，针对其存在的问题提出一些优化方案。我们分别探究了目标检测、图像分类和图像增强三个方面的技术，其中，我们提出了基于深度学习的卷积神经网络模型，利用该模型有效提高了图像检测和分类的准确性和效率；同时，我们提出了基于非线性滤波的图像增强算法，用于减少图像的失真和噪声，提高了图像处理的效果。
关键词：人工智能 图像识别 卷积神经网络 非线性滤波
Abstract
In recent years, with the rapid development of artificial intelligence, image recognition technology has been widely applied. However, in practical applications, there are still some problems such as data noise and image distortion, which directly affect the accuracy and efficiency of image recognition algorithms. Therefore, in this paper, we will conduct in-depth research on artificial intelligence based image recognition algorithms and propose some optimization solutions to address their existing problems. We explored three technologies: ob ject detection, image classification, and image enhancement. Among them, we proposed a convolutional neural network model based on deep learning, which effectively improved the accuracy and efficiency of image detection and classification; At the same time, we propose an image enhancement algorithm based on nonlinear filtering to reduce image distortion and noise, and improve the effectiveness of image processing.
Key Words：Artificial intelligence Image recognition Convolutional neural networks Nonlinear filtering
目 录
摘 要 I
Abstract II
引言 1
一、基本理论及相关技术 2
（一）图像识别的基本方法 2
（二）卷积神经网络技术 2
（三）非线性滤波技术 3
二、基于卷积神经网络的图像检测和分类算法 4
（一）卷积神经网络模型设计 4
（二）数据集处理和预训练 4
（三）实验结果分析 5
三、基于非线性滤波的图像增强算法 6
（一）图像失真和噪声的分析 6
（二）非线性滤波原理及方法 6
（三）模拟实验及结果分析 7
四、综合优化方案 8
（一）卷积神经网络和非线性滤波结合 8
（二）结果分析和总结 8
结 论 9
参 考 文 献 10
致 谢 11
