Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using
  a Novel Distributed Denoising CNN (DDCNN)
Sankar B. Mukil Saravanan Kalaivanan Kumar Siri Dubbaka
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.