摘 要
图像修复是计算机视觉领域的重要研究方向，旨在通过算法恢复受损或缺失的图像区域。随着深度学习技术的发展，其在图像修复中的应用逐渐成为研究热点。本研究以提升图像修复效果为目标，提出了一种基于生成对抗网络（GAN）的新型修复框架。该框架结合上下文感知机制与多尺度特征提取模块，有效解决了传统方法在纹理细节和结构一致性方面的不足。实验结果表明，所提方法在多种复杂场景下均表现出优异的修复性能，特别是在高分辨率图像处理中具有显著优势。此外，本研究还引入了自适应损失函数优化策略，进一步提升了模型的泛化能力。研究表明，深度学习驱动的图像修复技术不仅能够准确还原缺失内容，还能保持整体视觉质量，为实际应用提供了可靠的技术支持。这一成果为图像修复领域的理论发展和技术进步作出了重要贡献。关键词：图像修复; 生成对抗网络; 多尺度特征提取; 自适应损失函数; 深度学习
Abstract
Image inpainting is a critical research direction in the field of computer vision, aiming to restore damaged or missing regions of images through algorithms. With the development of deep learning technologies, their application in image inpainting has gradually become a research hotspot. This study proposes a novel inpainting fr amework based on Generative Adversarial Networks (GAN) with the goal of enhancing restoration performance. By integrating a context-aware mechanism and a multi-scale feature extraction module, the proposed fr amework effectively addresses the limitations of traditional methods in terms of texture details and structural consistency. Experimental results demonstrate that the method exhibits superior restoration capabilities across various complex scenarios, particularly showcasing significant advantages in high-resolution image processing. Additionally, this research introduces an adaptive loss function optimization strategy, which further improves the model's generalization ability. The findings indicate that deep learning-driven image inpainting not only accurately reconstructs missing content but also maintains overall visual quality, providing robust technical support for practical applications. This achievement makes an important contribution to both the theoretical development and technological advancement of the image inpainting domain.Key words:Image Restoration; Generative Adversarial Network; Multi-Scale Feature Extraction; Adaptive Loss Function; Deep Learning
目 录
摘 要 I
Abstract II
引 言 1
第1章、深度学习与图像修复基础 2
1.1、图像修复的基本概念 2
1.2、深度学习技术概述 2
1.3、深度学习在图像修复中的意义 3
第2章、深度学习模型在图像修复中的应用 4
2.1、卷积神经网络的应用研究 4
2.2、生成对抗网络的作用分析 4
2.3、自编码器在修复任务中的表现 5
第3章、图像修复的关键技术与挑战 6
3.1、数据预处理与增强方法 6
3.2、损失函数的设计与优化 6
3.3、高分辨率图像修复的技术难点 7
第4章、深度学习图像修复的实际案例与评估 8
4.1、文物图像修复的实践探索 8
4.2、医学影像修复的效果分析 8
4.3、图像修复性能的评价指标 8
结 论 10
参考文献 11
