Automatic Colorization of Grayscale Images with Diffusion Priors and Semantic Guidance

핵심 개념

The proposed automatic colorization pipeline leverages the generative capabilities of diffusion models and incorporates multimodal semantic priors to generate vivid and semantically plausible colors for grayscale images.

초록

The paper presents an automatic colorization pipeline that addresses the challenges of incorrect semantic colors and unsaturated colors in existing methods. The key components of the proposed approach are:

Colorization Diffusion Model:

Leverages the powerful generative capabilities of the diffusion model by incorporating pixel-level grayscale conditions in the latent space to ensure coherence and fidelity with the input grayscale image.

High-level Semantic Guidance:

Adopts multimodal semantic priors, including category, caption, and segmentation, to enhance the model's understanding of image content and generate vivid colors.
Injects the text and segmentation priors into the diffusion process through cross-attention and segmentation guidance, respectively.

Luminance-aware Decoder:

Designed to mitigate pixel-level distortion and make the reconstruction more faithful to the grayscale input by incorporating intermediate grayscale features from the encoder.

The experiments demonstrate that the proposed pipeline outperforms state-of-the-art methods in terms of perceptual realism and gains the highest human preference. The quantitative and qualitative results show that the pipeline can generate saturated and semantically plausible colors for grayscale images with complex content.

통계

The paper does not provide any specific numerical data or statistics in the main text. The quantitative comparisons are presented in terms of Fréchet Inception Distance (FID), colorfulness, and PSNR.

인용구

"Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors."
"We leverage the extraordinary generative ability of the diffusion prior to synthesize color with plausible semantics. To overcome the artifacts introduced by the diffusion prior, we apply the luminance conditional guidance."
"Besides, a luminance-aware decoder is designed to restore details and enhance overall visual quality. The proposed pipeline synthesizes saturated colors while maintaining plausible semantics."

핵심 통찰 요약

Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior

by Han Wang,Xin... 게시일 arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16678.pdf

더 깊은 질문

How can the proposed pipeline be extended to handle video colorization tasks?

To extend the proposed pipeline for video colorization tasks, several modifications and enhancements can be implemented:

Temporal Consistency: Incorporate temporal information to ensure color consistency across frames in a video sequence. This can involve leveraging techniques like optical flow to propagate color information between frames.
Frame Interpolation: Implement frame interpolation methods to generate color information for intermediate frames based on the colorization of key frames. This can help maintain smooth transitions in color across the video.
Efficient Processing: Optimize the pipeline for real-time processing of video data by considering parallel processing and efficient memory management techniques.
Dynamic Semantic Guidance: Develop dynamic semantic guidance mechanisms that can adapt to changing scenes and objects in videos, ensuring accurate and context-aware colorization.
Artifact Reduction: Implement post-processing techniques to reduce artifacts that may arise from frame-to-frame color variations or inconsistencies.

What are the potential limitations of the diffusion-based approach, and how can they be addressed in future research?

The diffusion-based approach, while powerful, may have some limitations that can be addressed in future research:

Computational Complexity: Diffusion models can be computationally intensive, especially for high-resolution images or video frames. Future research can focus on optimizing the model architecture and training procedures to reduce computational overhead.
Artifact Generation: Diffusion models may still generate artifacts or inconsistencies, especially in complex scenes or with intricate details. Research can explore advanced regularization techniques or additional conditioning mechanisms to mitigate these issues.
Scalability: Scaling diffusion models to handle large-scale datasets or video sequences efficiently can be a challenge. Future research can investigate scalable architectures or distributed training strategies to address scalability limitations.
Generalization: Diffusion models may struggle with generalizing to diverse datasets or unseen scenarios. Future research can focus on improving the model's generalization capabilities through data augmentation, domain adaptation, or meta-learning techniques.

How can the high-level semantic guidance module be further improved to better capture the complex relationships between image content and color semantics?

To enhance the high-level semantic guidance module for better capturing complex relationships between image content and color semantics, the following strategies can be considered:

Multi-Modal Fusion: Integrate multiple modalities of semantic information, such as textual descriptions, object categories, and spatial segmentation, in a unified framework to provide comprehensive guidance for colorization.
Attention Mechanisms: Implement advanced attention mechanisms that can dynamically focus on relevant semantic features while colorizing different regions of an image. This can improve the model's ability to capture intricate details and semantics.
Fine-Grained Semantic Parsing: Develop fine-grained semantic parsing techniques that can extract detailed semantic information at a pixel-level granularity, enabling more precise colorization based on semantic cues.
Interactive Guidance: Explore interactive or user-guided semantic guidance approaches where users can provide feedback or corrections during the colorization process, enhancing the model's understanding of complex color semantics.
Adaptive Semantic Embeddings: Develop adaptive semantic embedding techniques that can dynamically adjust the importance of different semantic cues based on the context of the image, allowing the model to adapt to varying degrees of semantic complexity.