将像素转化为杰作：采用新型分布式去噪CNN（DDCNN）的AI驱动艺术修复
Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)
AI总结：
针对传统艺术修复方法的局限，研究构建劣化艺术图像数据集，训练新型分布式去噪CNN（DDCNN）实现多类型多程度畸变去除与细节保留，经实验验证效果优于传统方法与其他去噪CNN，为文化遗产保护与艺术修复发展提供新方案。
摘要：
艺术修复对文化遗产保护至关重要，但传统方法在忠实还原原作的同时解决褪色、污渍、破损等问题方面存在局限性。我们提出一种创新方法，运用深度学习（具体为卷积神经网络（CNNs））与计算机视觉技术革新艺术修复领域。我们首先构建了一个包含多种畸变类型与退化程度的劣化艺术图像多样化数据集，利用该数据集训练分布式去噪CNN（DDCNN），使其在去除畸变的同时保留精细细节。我们的方法可适配不同畸变类型与程度，适用于绘画、素描、照片等各类劣化艺术品。大量实验表明，与其他去噪CNN模型相比，我们的方法兼具效率与有效性，可大幅减少畸变，将劣化艺术品修复为杰作。定量评估证实我们的方法优于传统技术，重塑艺术修复领域并助力文化遗产保护。综上，本文提出一种AI驱动的解决方案，将计算机视觉、深度学习与DDCNN相结合，可精准修复艺术品，克服现有局限，为艺术修复领域的未来发展铺平道路。
英文摘要：
Art restoration is crucial for preserving cultural heritage, but traditional methods have limitations in faithfully reproducing original artworks while addressing issues like fading, staining, and damage. We present an innovative approach using deep learning, specifically Convolutional Neural Networks (CNNs), and Computer Vision techniques to revolutionize art restoration. We start by creating a diverse dataset of deteriorated art images with various distortions and degradation levels. This dataset trains a Distributed Denoising CNN (DDCNN) to remove distortions while preserving intricate details. Our method is adaptable to different distortion types and levels, making it suitable for various deteriorated artworks, including paintings, sketches, and photographs. Extensive experiments demonstrate our approach's efficiency and effectiveness compared to other Denoising CNN models. We achieve a substantial reduction in distortion, transforming deteriorated artworks into masterpieces. Quantitative evaluations confirm our method's superiority over traditional techniques, reshaping the art restoration field and preserving cultural heritage. In summary, our paper introduces an AI-powered solution that combines Computer Vision and deep learning with DDCNN to restore artworks accurately, overcoming limitations and paving the way for future advancements in art restoration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
