大学本科毕业论文

发布时间：2026年03月15日作者:aiycxz.cn

题目基于深度学习的图像去噪研究学院信息科学与工程学院专业通信工程毕业届别 2020姓名刘文强导师王海涌职称教授基于深度学习的图像去噪研究摘要：图像去噪是图像处理领域中的一个重要研究方向，其目的是从含有噪声的图像中恢复出原始图像。传统的图像去噪方法通常基于数学模型和信号处理技术，如滤波、小波变换等。然而，这些方法在处理复杂噪声和保留图像细节方面存在一定的局限性。近年来，深度学习技术的快速发展为图像去噪提供了新的解决方案。深度学习模型，特别是卷积神经网络（CNN），通过自动学习图像特征和噪声模式，能够更有效地去除噪声并保留图像细节。本文旨在研究基于深度学习的图像去噪方法，通过构建和训练深度学习模型，实现对噪声图像的高质量去噪。研究内容包括以下几个方面：首先，介绍图像去噪的背景和意义，分析传统去噪方法的优缺点；其次，详细阐述深度学习的基本原理和常用模型，特别是卷积神经网络的结构和训练方法；然后，设计并实现基于深度学习的图像去噪模型，包括数据集的准备、模型架构的设计、损失函数的选择以及训练策略的优化；最后，通过实验验证所提方法的有效性，并与传统去噪方法进行对比分析。实验结果表明，基于深度学习的图像去噪方法在去除噪声和保留图像细节方面优于传统方法。深度学习模型能够自适应地学习噪声特征，并在不同噪声水平下保持较好的去噪效果。此外，本文还探讨了模型在不同类型噪声下的表现，验证了其鲁棒性和泛化能力。关键词：图像去噪；深度学习；卷积神经网络；噪声去除；图像恢复# Research on Image Denoising Based on Deep Learning**Abstract:** Image denoising is an important research direction in the field of image processing, aiming to recover the original image from a noisy image. Traditional image denoising methods are typically based on mathematical models and signal processing techniques, such as filtering and wavelet transform. However, these methods have certain limitations in handling complex noise and preserving image details. In recent years, the rapid development of deep learning technology has provided new solutions for image denoising. Deep learning models, particularly convolutional neural networks (CNNs), can more effectively remove noise and preserve image details by automatically learning image features and noise patterns.This paper aims to study deep learning-based image denoising methods by constructing and training deep learning models to achieve high-quality denoising of noisy images. The research content includes the following aspects: first, introducing the background and significance of image denoising, and analyzing the advantages and disadvantages of traditional denoising methods; second, elaborating on the basic principles and common models of deep learning, particularly the structure and training methods of convolutional neural networks; then, designing and implementing a deep learning-based image denoising model, including dataset preparation, model architecture design, loss function selection, and optimization of training strategies; finally, validating the effectiveness of the proposed method through experiments and conducting comparative analysis with traditional denoising methods.The experimental results show that deep learning-based image denoising methods outperform traditional methods in terms of noise removal and detail preservation. Deep learning models can adaptively learn noise features and maintain good denoising performance under different noise levels. Additionally, this paper explores the model's performance under different types of noise, verifying its robustness and generalization ability.**Keywords:** Image denoising; Deep learning; Convolutional neural network; Noise removal; Image restoration# 目录## 1 绪论1.1 研究背景与意义 ...... 1 1.2 国内外研究现状 ...... 2 1.3 研究内容与结构安排 ...... 3 ## 2 图像去噪相关理论2.1 图像噪声模型 ...... 5 2.2 传统图像去噪方法 ...... 6 2.3 深度学习基础 ...... 8 ## 3 基于深度学习的图像去噪模型设计3.1 数据集准备与预处理 ...... 11 3.2 模型架构设计 ...... 12 3.3 损失函数与优化算法 ...... 14 ## 4 实验设计与结果分析4.1 实验环境与参数设置 ...... 16 4.2 实验结果与分析 ...... 17 4.3 与传统方法的对比 ...... 19 ## 5 结论与展望5.1 研究总结 ...... 21 5.2 未来工作展望 ...... 22 参考文献 ...... 23 致谢 ...... 25 兰州交通大学本科毕业论文1 绪论1.1 研究背景与意义图像去噪是图像处理领域中的一个基础且重要的研究方向，其目标是从受到噪声污染的图像中恢复出原始清晰的图像。噪声可能来源于图像采集、传输或存储过程中的各种因素，如传感器噪声

大学本科毕业论文

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