Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks

• URL: http://arxiv.org/abs/2508.05068v1
• Date: Thu, 07 Aug 2025 06:41:31 GMT
• Title: Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks
• Authors: Ruiyu Li, Changyuan Qiu, Hangrui Cao, Qihan Ren, Yuqing Qiu,
• Abstract summary: This project explores automatic image colorization via classification and adversarial learning.<n>We will build our models on prior works, apply modifications for our specific scenario and make comparisons.
• Score: 5.331915414669663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image colorization, the task of adding colors to grayscale images, has been the focus of significant research efforts in computer vision in recent years for its various application areas such as color restoration and automatic animation colorization [15, 1]. The colorization problem is challenging as it is highly ill-posed with two out of three image dimensions lost, resulting in large degrees of freedom. However, semantics of the scene as well as the surface texture could provide important cues for colors: the sky is typically blue, the clouds are typically white and the grass is typically green, and there are huge amounts of training data available for learning such priors since any colored image could serve as a training data point [20]. Colorization is initially formulated as a regression task[5], which ignores the multi-modal nature of color prediction. In this project, we explore automatic image colorization via classification and adversarial learning. We will build our models on prior works, apply modifications for our specific scenario and make comparisons.

Related papers

• Automatic Controllable Colorization via Imagination [55.489416987587305]
  We propose a framework for automatic colorization that allows for iterative editing and modifications. By understanding the content within a grayscale image, we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring, mimicking the process of human experts.
  arXiv  Detail & Related papers  (2024-04-08T16:46:07Z)
• Diffusing Colors: Image Colorization with Text Guided Diffusion [11.727899027933466]
  We present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts. Our method provides a balance between automation and control, outperforming existing techniques in terms of visual quality and semantic coherence. Our approach holds potential particularly for color enhancement and historical image colorization.
  arXiv  Detail & Related papers  (2023-12-07T08:59:20Z)
• Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model [0.0]
  This paper presents a novel approach that combines the advantages of transfer and ensemble learning approaches to help reduce training time and resource requirements. The proposed model shows promising results, with accuracy ranging from 94.55% to 99.13%.
  arXiv  Detail & Related papers  (2023-09-25T19:22:57Z)
• TIC: Text-Guided Image Colorization [24.317541784957285]
  We propose a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-08-04T18:40:20Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• Astronomical Image Colorization and upscaling with Generative Adversarial Networks [0.0]
  This research aims to provide an automated approach for the problem by focusing on a very specific domain of images, namely astronomical images. We explore the usage of various models in two different color spaces, RGB and L*a*b. The model produces visually appealing images which hallucinate high resolution, colorized data in these results which does not exist in the original image.
  arXiv  Detail & Related papers  (2021-12-27T19:01:20Z)
• Image Colorization: A Survey and Dataset [94.59768013860668]
  This article presents a comprehensive survey of state-of-the-art deep learning-based image colorization techniques. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one.
  arXiv  Detail & Related papers  (2020-08-25T01:22:52Z)
• Semantic-driven Colorization [78.88814849391352]
  Recent colorization works implicitly predict the semantic information while learning to colorize black-and-white images. In this study, we simulate that human-like action to let our network first learn to understand the photo, then colorize it.
  arXiv  Detail & Related papers  (2020-06-13T08:13:30Z)
• Instance-aware Image Colorization [51.12040118366072]
  In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images. We use a similar network to extract the full-image features and apply a fusion module to predict the final colors.
  arXiv  Detail & Related papers  (2020-05-21T17:59:23Z)
• Learning to Structure an Image with Few Colors [59.34619548026885]
  We propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. With only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime.
  arXiv  Detail & Related papers  (2020-03-17T17:56:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.