The Power of Context: How Multimodality Improves Image Super-Resolution

2025-03-18

AI Summary

Single-image super-resolution (SISR) struggles to simultaneously achieve fine-detail recovery and high perceptual quality, primarily due to reliance on weak image priors in conventional methods. To address this, we propose a diffusion-based multimodal guided super-resolution framework that enables plug-and-play dynamic fusion of heterogeneous modalities—including depth, semantic segmentation, edge maps, and text—for the first time. Our approach introduces a spatially constrained text-conditioning mechanism to suppress hallucination and a differentiable modality-weight controller for independent, fine-grained adjustment and controllable editing of each modality’s influence. By leveraging cross-modal attention alignment and condition-guided sampling, our method significantly enhances visual realism, structural fidelity, and semantic consistency of reconstructed images. It outperforms state-of-the-art generative SISR methods across multiple benchmarks and enables novel applications such as depth-driven defocusing and segmentation-guided object enhancement.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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/.

Problem

• Improves image super-resolution using multimodal contextual information.
• Mitigates hallucinations in text-guided image generation.
• Enhances visual quality and fidelity in super-resolution outputs.

Innovation

• Leverages multimodal data for image super-resolution
• Uses diffusion model with flexible network architecture
• Controls modality guidance strength independently