The Power of Context: How Multimodality Improves Image Super-Resolution (2503.14503v1)

Abstract: Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inputs. Existing methods often rely on limited image priors, leading to suboptimal results. We propose a novel approach that leverages the rich contextual information available in multiple modalities -- including depth, segmentation, edges, and text prompts -- to learn a powerful generative prior for SISR within a diffusion model framework. We introduce a flexible network architecture that effectively fuses multimodal information, accommodating an arbitrary number of input modalities without requiring significant modifications to the diffusion process. Crucially, we mitigate hallucinations, often introduced by text prompts, by using spatial information from other modalities to guide regional text-based conditioning. Each modality's guidance strength can also be controlled independently, allowing steering outputs toward different directions, such as increasing bokeh through depth or adjusting object prominence via segmentation. Extensive experiments demonstrate that our model surpasses state-of-the-art generative SISR methods, achieving superior visual quality and fidelity. See project page at https://mmsr.kfmei.com/.

Understanding the Role of Multimodality in Image Super-Resolution

The paper "The Power of Context: How Multimodality Improves Image Super-Resolution" details a sophisticated approach to the single-image super-resolution (SISR) challenge by leveraging multimodal information. This paper presents a method, coined as Multimodal Super-Resolution (MMSR), that uses a combination of depth, segmentation, edges, and text information within the context of a diffusion model framework. The authors propose a novel network architecture that fuses these modalities to produce superior high-resolution images from low-resolution inputs. Crucially, this approach addresses notable challenges such as minimizing hallucinations, which are common in generative models, by aligning spatial information from multiple sources.

Methodological Overview

This paper's methodology innovatively combines the following elements to enhance SISR:

• Multimodal Integration: The MMSR approach harmonizes multiple data types—text captions, depth maps, semantic segmentation, and edge information—allowing it to capture additional contextual data that surpasses the limitations of single-modality methods.
• Network Architecture: A flexible network design permits the seamless integration of diverse modalities into the diffusion process. This architecture effectively manages the complexity inherent in multimodal inputs, maintaining efficiency and adaptability.
• Guidance and Control: The authors introduce a multimodal classifier-free guidance mechanism to fine-tune the model's output, manipulating the impact of each modality. This capability empowers fine-grained control over the SISR process, such as creating a bokeh effect or modifying object prominence.

Experimental Results

The paper provides extensive experimental results demonstrating that MMSR outperforms contemporary generative models across various benchmarks. Notably, the MMSR method:

• Achieves superior perceptual quality metrics, including LPIPS and DISTS.
• Exhibits high levels of visual realism and fidelity due to its innovative multimodal guidance.
• Outperforms state-of-the-art models in managing intricate details, evident in both synthetic and real-world super-resolution benchmarks.

Implications and Future Directions

The implications of this research are manifold, opening avenues for more nuanced image restoration workflows in practical applications, such as medical imaging, satellite imagery analysis, and surveillance systems. By reducing hallucinations and integrating contextual spatial understanding, this approach aligns closer with human perception than traditional models.

For future development, the research paves the way for integrating faster vision-language components, potentially enhancing real-time performance. Furthermore, optimizing the robustness of multimodal components may provide resilience against degraded or incomplete inputs, which often occur in practical scenarios.

The convergence of multimodal data with machine learning in this paper reflects a significant trajectory in AI research, emphasizing a more holistic understanding of input data for complex image processing tasks. This research underscores the gradual shift towards architectures that embrace and utilize the depth and breadth of multimodal information, hinting at a future where AI systems approach the elasticity of human perception.