The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. At last, we discuss some emerging topics and open problems about image denoising.ĭealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. Then, we review conventional sparse representation based denoising algorithms, low-rank based denoising algorithms and recently proposed deep neural networks based approaches. We firstly present an overview of prior modeling approaches used in image denoising task. In this paper, we briefly review recent progresses in image denoising.

Also, image denoising constitutes an ideal test bed for evaluating image prior modeling methods. It represents an important building block in real applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment. Image denoising, which aims to reconstruct a high quality image from its degraded observation, is a classical yet still very active topic in the area of low-level computer vision. Although the development of hardware has steadily improved the quality of images for the last several decades, image degradation is unavoidable due to the many factors affecting the image acquisition process and the subsequent post-processing. The recent advances in hardware and imaging systems made the digital cameras ubiquitous. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.

The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018.

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. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Recent strategies make use of generative models to impute missing or corrupted data. The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications.

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