Inpainting and Denoising Challenges

Description

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/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. Front Matter ....Pages i-viii A Brief Review of Image Denoising Algorithms and Beyond (Shuhang Gu, Radu Timofte)....Pages 1-21 ChaLearn Looking at People: Inpainting and Denoising Challenges (Sergio Escalera, Martí Soler, Stephane Ayache, Umut Güçlü, Jun Wan, Meysam Madadi et al.)....Pages 23-44 U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting (Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, Anxiao (Andrew) Jiang)....Pages 45-50 FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks (Sukesh Adiga V, Jayanthi Sivaswamy)....Pages 51-61 Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising (Le Manh Quan, Yong-Guk Kim)....Pages 63-76 Video DeCaptioning Using U-Net with Stacked Dilated Convolutional Layers (Shivansh Mundra, Arnav Kumar Jain, Sayan Sinha)....Pages 77-86 Joint Caption Detection and Inpainting Using Generative Network (Vismay Patel, Anubha Pandey)....Pages 87-94 Generative Image Inpainting for Person Pose Generation (Vismay Patel, Anubha Pandey)....Pages 95-100 Person Inpainting with Generative Adversarial Networks (Gizem Esra Ünlü)....Pages 101-110 Road Layout Understanding by Generative Adversarial Inpainting (Lorenzo Berlincioni, Federico Becattini, Leonardo Galteri, Lorenzo Seidenari, Alberto Del Bimbo)....Pages 111-128 Photo-Realistic and Robust Inpainting of Faces Using Refinement GANs (Dejan Malesevic, Christoph Mayer, Shuhang Gu, Radu Timofte)....Pages 129-144