Image Colorization: A Survey and Dataset (2008.10774v4)

Abstract: Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for image colorization have significantly progressed, necessitating a systematic survey and benchmarking of these techniques. This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques, describing their fundamental block architectures, inputs, optimizers, loss functions, training protocols, training data, etc. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance, such as benchmark datasets and evaluation metrics. We highlight the limitations of existing datasets and introduce a new dataset specific to colorization. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one. Finally, we discuss the limitations of existing methods and recommend possible solutions and future research directions for this rapidly evolving topic of deep image colorization. The dataset and codes for evaluation are publicly available at https://github.com/saeed-anwar/ColorSurvey.

• The paper systematically reviews state-of-the-art deep learning models for image colorization, categorizing them into seven distinct classes.
• It introduces the Natural-Color Dataset (NCD) specifically curated to address the shortcomings of general image datasets in benchmarking colorization techniques.
• The authors advocate for new evaluation metrics and the integration of advanced mechanisms like GANs and attention modules to enhance model precision.

Image Colorization: A Survey and Dataset

The scholarly article "Image Colorization: A Survey and Dataset," authored by Saeed Anwar et al., provides a comprehensive survey of state-of-the-art techniques for the colorization of images using deep learning models. The significance of this paper lies in its structured overview of novel advancements in the field, offering meaningful insights into the various methodologies of image colorization, the classification of these methodologies into specific categories, the challenges faced by existing models, and the introduction of a new dataset to benchmark colorization techniques.

Image colorization, the task of estimating RGB colors for grayscale images, has been widely explored using deep learning paradigms over the past decade, thereby necessitating an up-to-date survey to encapsulate the present status of research in this area. The paper begins by categorizing the different approaches to image colorization into seven distinct classes: plain networks, user-guided networks, domain-specific colorization, text-based colorization, diverse colorization, multipath networks, and exemplar-based colorization. Each classification is discussed with detailed architectural specifications, evaluation metrics, strengths, and inherent weaknesses, offering readers a meticulous understanding of the design choices and implementation challenges in these networks.

Key findings from the paper reveal an over-reliance on existing image datasets that are not specific to the task of colorization. In response, the authors have introduced the Natural-Color Dataset (NCD), a collection explicitly curated for assessing image colorization methods. This dataset aims to standardize the evaluation process and address pitfalls in current datasets that have compromised the comprehensive evaluation of colorization techniques. The authors advocate for the advancement of robust and more precise evaluation metrics tailored specifically for color metrics, contrasting the prevalent use of PSNR and SSIM from other image processing tasks.

The practical implications of this research are visible in several areas. From aesthetic enhancements of legacy media to improvements in automated image processing systems, colorization serves as a pivotal process with wide-ranging applications in photography, visual arts, and beyond. The theoretical contributions messaged by the paper also lie in propelling unsupervised and semi-supervised research directions, emphasizing the utility of GANs, and advocating for the involvement of innovative attention mechanisms in upcoming techniques.

By introducing a new dataset and offering an exhaustive analysis of the current methodologies, the authors set a foundational platform towards systematic benchmarking and comparison of image colorization techniques. As future work, the authors suggest exploring augmented reality contexts and developing adaptive learning models that robustly handle colorization in diverse scenarios and under varying environmental conditions.

This paper is an essential resource for experienced researchers looking to deepen their understanding of image colorization strategies and it will likely act as a catalyst for further research in refining the automation capabilities and accuracy of colorization systems, revealing its broader potential across AI-based applications.