iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer (2207.06831v4)

Abstract: Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in real-time by utilizing pixel shuffling, an efficient upsampling technique that replaces the decoder architecture. Also, in order to mitigate the artifacts caused by pixel shuffling with large upsampling ratios, we present the local stabilizing layer. Extensive quantitative and qualitative results demonstrate that our approach highly outperforms existing methods for point-interactive colorization, producing accurately colorized images with a user's minimal effort. Official codes are available at https://pmh9960.github.io/research/iColoriT

• The paper introduces Vision Transformers to propagate local color hints globally, overcoming the limitations of CNN-based methods.
• It employs pixel shuffling and a local stabilizing layer to enhance upsampling efficiency and minimize color artifacts.
• Extensive evaluations show notable improvements in PSNR and LPIPS, highlighting its potential for interactive image editing applications.

Overview of "iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer"

The academic paper introduces iColoriT, an innovative approach to point-interactive image colorization. The primary goal is to enable efficient propagation of user-provided color hints across grayscale images to achieve visually appealing colorization with minimal user input. Traditional convolutional neural network (CNN) based methods often face limitations in effectively transferring user color hints to distant image regions, resulting in partially colorized images. iColoriT addresses this challenge by leveraging the global receptive field inherent in Vision Transformers (ViTs), offering a new pathway for color hint propagation.

Contributions and Methodology

1. Vision Transformer for Colorization: iColoriT is one of the first frameworks to employ Vision Transformers for the task of point-interactive colorization. The global receptive field, facilitated by Transformer's self-attention mechanism, allows the model to propagate user hints more efficiently across the image compared to localized CNN-based methods.
2. Efficient Upsampling with Pixel Shuffling: To achieve real-time performance, iColoriT utilizes pixel shuffling—an upsampling operation that rearranges feature maps to increase spatial resolution. This eliminates the need for a conventional decoder architecture, substantially reducing computational costs while maintaining imagery quality.
3. Local Stabilizing Layer: iColoriT introduces a local stabilizing layer to mitigate artifacts often introduced by large upsampling ratios. This layer ensures that adjacent patches have consistent colors, overcoming issues like visible patch boundaries that could detract from image realism.
4. Performance Metrics: The paper provides extensive quantitative evaluations demonstrating that iColoriT outperforms existing state-of-the-art methods in both accuracy and user hint efficiency. In terms of Peak Signal-to-Noise Ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS), iColoriT shows significant performance gains.

Implications and Future Directions

The introduction of iColoriT opens up several implications for both theoretical advancements and practical applications:

• Theoretical Implications: The paper successfully integrates ViTs into the domain of image colorization, traditionally dominated by CNNs. This advancement suggests potential for Transformers in various image processing tasks where global contextual understanding is critical. Future research could further optimize Transformer efficiency or develop hybrid architectures combining CNNs and Transformers.
• Practical Applications: iColoriT can be a valuable tool in photography and media production where colorization tasks are frequent. Real-time performance allows it to be integrated into interactive applications, making it a candidate for commercial software tools.
• Limitations and Next Steps: While iColoriT excels in propagating hints over complex image structures, there are limitations in colorizing very small or detailed regions without semantic labels. Future work could integrate semantic understanding to refine these aspects further.

Overall, the paper presents a carefully constructed framework that effectively utilizes recent advancements in deep learning architecture for practical application in image colorization, setting grounds for further exploration and refinement in Transformer-based image processing tasks.