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CoSeR: Bridging Image and Language for Cognitive Super-Resolution (2311.16512v4)

Published 27 Nov 2023 in cs.CV and cs.AI

Abstract: Existing super-resolution (SR) models primarily focus on restoring local texture details, often neglecting the global semantic information within the scene. This oversight can lead to the omission of crucial semantic details or the introduction of inaccurate textures during the recovery process. In our work, we introduce the Cognitive Super-Resolution (CoSeR) framework, empowering SR models with the capacity to comprehend low-resolution images. We achieve this by marrying image appearance and language understanding to generate a cognitive embedding, which not only activates prior information from large text-to-image diffusion models but also facilitates the generation of high-quality reference images to optimize the SR process. To further improve image fidelity, we propose a novel condition injection scheme called "All-in-Attention", consolidating all conditional information into a single module. Consequently, our method successfully restores semantically correct and photorealistic details, demonstrating state-of-the-art performance across multiple benchmarks. Code: https://github.com/VINHYU/CoSeR

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Authors (8)
  1. Haoze Sun (21 papers)
  2. Wenbo Li (115 papers)
  3. Jianzhuang Liu (91 papers)
  4. Haoyu Chen (71 papers)
  5. Renjing Pei (26 papers)
  6. Xueyi Zou (16 papers)
  7. Youliang Yan (31 papers)
  8. Yujiu Yang (155 papers)
Citations (32)
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Summary

  • The paper introduces cognitive embeddings that merge image details with language context to overcome traditional super-resolution limitations.
  • It employs an All-in-Attention mechanism that effectively integrates semantic cues for photorealistic texture restoration.
  • Empirical evaluations show state-of-the-art performance across benchmarks, highlighting its potential for real-world applications.

Bridging Image and Language for Enhanced Super-Resolution: An Analysis of CoSeR Framework

The paper introduces Cognitive Super-Resolution (CoSeR), a novel framework designed to enhance the super-resolution (SR) of images by integrating cognitive understanding through a combined image and language approach. The CoSeR framework stands out in its capacity to not only restore traditional fine texture details but also comprehend and exploit the global semantic context that is often ignored by existing SR techniques.

The prevailing challenges in real-world image SR, such as preserving semantic integrity and avoiding the introduction of erroneous textures, are addressed by CoSeR through its unique methodological innovations. It bridges low-level image processing with high-level cognition by creating cognitive embeddings that facilitate the generation of high-quality reference images. This is achieved by leveraging large text-to-image diffusion models, thereby extracting essential cognitive information from low-resolution images. The CoSeR framework also introduces an innovative condition injection technique, "All-in-Attention," which integrates this cognitive embodiment into the SR process, ensuring that the output is photorealistic and semantically accurate.

Methodological Advancements

CoSeR introduces several key innovations:

  1. Cognitive Embeddings: By harmonizing image details with language understanding, CoSeR develops a cognitive embedding that synthesizes semantic and appearance information. This embedding activates pre-existing knowledge from diffusion models, optimizing SR tasks.
  2. All-in-Attention Framework: This condition injection scheme concentrates all conditional inputs into a single attention mechanism, enhancing the effective restoration of semantically-rich and photorealistic details.
  3. Generation of Reference Images: CoSeR constructs a high-quality reference image aligned with the low-resolution input in semantics and texture. This reference significantly aids the super-resolution process, as demonstrated through extensive empirical evaluations.
  4. Benchmark Performance: Across multiple established benchmarks, CoSeR has demonstrated state-of-the-art SR capability, outperforming existing models in terms of fidelity and realism.

Implications in the Field

The CoSeR framework has profound implications for fields relying heavily on visual detail restoration, such as mobile photography, autonomous driving, and robotic vision. Its ability to enhance images by embedding cognitive properties opens up new avenues for developing SR models that are not only context-aware but also capable of handling complex real-world scenes more realistically.

The methodologies proposed suggest future directions in leveraging LLMs not only to enhance texture restorement but also to improve semantic understanding in other image processing tasks. The same cognitive embedding techniques could be applied to tasks such as image deblurring, enhancement under varying light conditions, and even in areas requiring the synthesis of missing content, such as context-aware inpainting.

Future Developments

Looking forward, the principles underlying CoSeR may pave the way for more efficient models that require fewer computational resources while maintaining high SR quality. Additionally, exploring the possibility of real-time application and reducing dependency on large diffusion models are potential areas for further research. Enhancing the precision of cognitive embeddings through more sophisticated LLMs and broad training datasets could further improve model robustness across different domains.

In summary, the CoSeR framework marks a significant evolution in cognitive image processing by marrying the strengths of textual and visual understanding, demonstrating how advanced AI techniques can be creatively harnessed to address longstanding challenges in super-resolution. The results and approaches proposed in this paper contribute valuable insights to the ongoing development of intelligent, adaptive image restoration solutions.

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  1. GitHub - VINHYU/CoSeR (349 stars)