Inpainting and Denoising Challenges

• Corporate Author: SpringerLink (Online service)
• Other Authors: Escalera, Sergio (Editor, http://id.loc.gov/vocabulary/relators/edt), Ayache, Stephane (Editor, http://id.loc.gov/vocabulary/relators/edt), Wan, Jun (Editor, http://id.loc.gov/vocabulary/relators/edt), Madadi, Meysam (Editor, http://id.loc.gov/vocabulary/relators/edt), Güçlü, Umut (Editor, http://id.loc.gov/vocabulary/relators/edt), Baró, Xavier (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: The Springer Series on Challenges in Machine Learning,
• Subjects: Artificial intelligence.; Optical data processing.; Pattern recognition.; Artificial Intelligence.; Image Processing and Computer Vision.; Pattern Recognition.

The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/v...