Real-Time User-Guided Image Colorization with Learned Deep Priors (1705.02999v1)

Abstract: We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer. Our code and models are available at https://richzhang.github.io/ideepcolor.

• The paper introduces an end-to-end deep learning model that fuses sparse user hints with automated color predictions for real-time image colorization.
• It employs two network variants—Local Hints for precise user inputs and Global Hints for broader color statistics—enhancing usability.
• Extensive experiments, including PSNR evaluations and user studies, validate its competitive performance against state-of-the-art methods.

Real-Time User-Guided Image Colorization with Learned Deep Priors

The paper "Real-Time User-Guided Image Colorization with Learned Deep Priors" presents a novel approach for interactive image colorization utilizing deep learning techniques. Unlike previous methods that heavily depend on manual user inputs or automatic predictions, this research seeks to amalgamate the strengths of both paradigms to facilitate real-time user-guided colorization using Convolutional Neural Networks (CNNs).

Methodology and Contributions

The core of the proposed system is a CNN that directly maps a grayscale image, along with sparse user "hints," to a fully colorized image. These hints are efficiently propagated through low-level cues and high-level semantic information learned from large-scale data. The paper outlines two variants of the network: the Local Hints Network which incorporates precise user-defined points and the Global Hints Network operating with broader global color statistics such as histograms or average saturation.

Key contributions of this research include:

1. End-to-End Learning of User Inputs: The system fuses sparse user points with automatic colorization, allowing users to guide colorization with minimal effort. These user interactions are simulated synthetically during training, bypassing the need for extensive real user interaction datasets.
2. Data-Driven Color Palette: The system suggests the most probable local colors at any given location, assisting users in making informed decisions, thus enhancing the interface's usability and reducing the cognitive load on users.
3. Real-Time Processing: The entire colorization process is achieved in a single feed-forward network pass, allowing for real-time interaction, which is practical and user-friendly.
4. Versatility in Application: The method demonstrates proficiency in adjusting not only to natural colorizations but also to specific user demands, such as uncommon or artistic colorizations.

Experimentation and Results

The authors evaluate the efficacy of their system through both automatic assessments and user studies. They observe that even novice users, within a constrained period and with minimal training, can achieve convincing colorizations, significantly improving automatic baselines. Notably, the Global Hints Network effectively integrates input global statistics to refine the colorization outcome, showcasing flexibility in handling diverse input conditions.

The paper further highlights quantitative robustness through PSNR evaluations which demonstrate the model's competitive performance against other state-of-the-art methods. The innovative use of max-error sampling for input points further emphasizes the system's adaptability to enhance user-guided interactions.

Implications and Future Directions

The implications of this research are multifaceted. Practically, it presents a tool that can streamline colorization tasks in artistic, restoration, and archival projects, increasing efficiency without compromising quality. Theoretically, it opens pathways for exploring how user interactions can be further integrated into deep learning frameworks, emphasizing the need for models that are not only predictive but also adaptable and interactive.

Future developments could focus on refining the model's capacity to handle more complex inputs, such as diverse brush strokes, and on personalizing the system further to accommodate individual user styles and preferences. Additionally, extending such frameworks to other interactive media applications could leverage the strengths of learned deep priors in novel ways.

In conclusion, this paper provides a substantial contribution to the field of interactive computer graphics and image processing, proposing a refined model that balances automated processes with nuanced user control. This research not only enhances the technical paradigm of colorization but also contributes to the broader dialogue around human-computer interaction in AI systems.