DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders (2212.11613v5)

Abstract: Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness. Directly training a deep neural network usually leads to incorrect semantic colors and low color richness. While transformer-based methods can deliver better results, they often rely on manually designed priors, suffer from poor generalization ability, and introduce color bleeding effects. To address these issues, we propose DDColor, an end-to-end method with dual decoders for image colorization. Our approach includes a pixel decoder and a query-based color decoder. The former restores the spatial resolution of the image, while the latter utilizes rich visual features to refine color queries, thus avoiding hand-crafted priors. Our two decoders work together to establish correlations between color and multi-scale semantic representations via cross-attention, significantly alleviating the color bleeding effect. Additionally, a simple yet effective colorfulness loss is introduced to enhance the color richness. Extensive experiments demonstrate that DDColor achieves superior performance to existing state-of-the-art works both quantitatively and qualitatively. The codes and models are publicly available at https://github.com/piddnad/DDColor.

DDColor: An Expert Overview

The paper "DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders," authored by Xiaoyang Kang et al. from the DAMO Academy at Alibaba Group, presents an innovative approach to the automatic colorization of grayscale images. This method addresses key challenges such as multi-modal uncertainty and high ill-posedness inherent in the image colorization task. Unlike traditional models, the DDColor framework employs a dual-decoder structure consisting of both a pixel decoder and a query-based color decoder that work in tandem to produce semantically consistent and visually rich color outputs.

Key Contributions and Methodology

1. Dual Decoders for Enhanced Colorization: The DDColor framework introduces a novel dual-decoder architecture. The pixel decoder focuses on restoring the spatial resolution of image features, while the query-based color decoder utilizes multi-scale visual features to refine adaptive color queries. This dual-decoder approach significantly reduces color bleeding effects and enhances the semantic consistency of colorization results.
2. Query-Based Color Decoder: Traditional transformer-based colorization methods often rely on manually designed priors. In contrast, DDColor leverages a query-based transformer as its color decoder. This component learns color queries in an end-to-end manner without the need for handcrafted priors, thus improving generalization and adaptability to diverse image contexts.
3. Multi-Scale Feature Integration: DDColor employs visual features extracted at multiple scales within the color decoder. This strategy ensures that the model captures both high-level semantic information and fine-grained details, leading to more accurate and contextually appropriate colorization.
4. Colorfulness Loss: To improve color richness, the authors introduce a colorfulness loss inspired by perceptual metrics, which enhances the vividness of the generated images without compromising the semantic accuracy of the colorization.

Experimental Validation

The performance of the DDColor framework is thoroughly validated through extensive experiments on public benchmarks like ImageNet, COCO-Stuff, and ADE20K. Quantitative metrics such as Fréchet Inception Distance (FID), colorfulness score (CF), and Peak Signal-to-Noise Ratio (PSNR) demonstrate the superiority of DDColor compared to existing state-of-the-art methods. Notably, DDColor achieves the lowest FID scores across all datasets, indicating high-quality, photo-realistic colorization results.

Visual Comparisons and User Studies

Qualitative comparisons reveal that DDColor consistently produces more natural and vibrant colors while maintaining semantic coherence across various objects and scenes. A user paper further corroborates these findings, with human observers showing a preference for DDColor's results over those generated by competing methods.

Theoretical and Practical Implications

The proposed DDColor framework has significant theoretical implications. By introducing adaptive color queries and a multi-scale attention mechanism, the model advances the understanding of how deep learning can be leveraged for complex image restoration tasks. Practically, DDColor can be applied to a wide range of real-world applications, such as legacy photo restoration, video remastering, and artistic image editing.

Future Developments

The success of DDColor opens several avenues for future research. Improving the model's ability to handle complex scenarios involving transparent or translucent objects could further enhance its robustness. Additionally, integrating user control mechanisms, such as interactive tools for specifying color hints or using text-based guidance, could increase its applicability in artistic and commercial domains.

Conclusion

DDColor by Xiaoyang Kang et al. is a significant contribution to the field of image colorization, boasting a robust dual-decoder architecture that markedly improves color richness and semantic accuracy. Its innovative design and impressive performance highlight its potential for both academic exploration and practical deployment in various image restoration applications. The model sets a new benchmark for automatic colorization methods, paving the way for future advancements in the domain.