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Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks

Weixiong Sun, Xiang Yin, Chao Dong

Abstract

Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restoration. In this work, we conduct a systematic evaluation of Nano Banana 2 for image restoration across diverse scenes and degradation types. Our results show that prompt design plays a critical role, where concise prompts with explicit fidelity constraints achieve the best trade-off between reconstruction accuracy and perceptual quality. Compared with state-of-the-art restoration models, Nano Banana 2 achieves superior performance in full-reference metrics while remaining competitive in perceptual quality, which is further supported by user studies. We also observe strong generalization in challenging scenarios, such as small faces, dense crowds, and severe degradations. However, the model remains sensitive to prompt formulation and may require iterative refinement for optimal results. Overall, our findings suggest that general-purpose generative models hold strong potential as unified image restoration solvers, while highlighting the importance of controllability and robustness. All test results are available on https://github.com/yxyuanxiao/NanoBanana2TestOnIR.

Can Nano Banana 2 Replace Traditional Image Restoration Models? An Evaluation of Its Performance on Image Restoration Tasks

Abstract

Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restoration. In this work, we conduct a systematic evaluation of Nano Banana 2 for image restoration across diverse scenes and degradation types. Our results show that prompt design plays a critical role, where concise prompts with explicit fidelity constraints achieve the best trade-off between reconstruction accuracy and perceptual quality. Compared with state-of-the-art restoration models, Nano Banana 2 achieves superior performance in full-reference metrics while remaining competitive in perceptual quality, which is further supported by user studies. We also observe strong generalization in challenging scenarios, such as small faces, dense crowds, and severe degradations. However, the model remains sensitive to prompt formulation and may require iterative refinement for optimal results. Overall, our findings suggest that general-purpose generative models hold strong potential as unified image restoration solvers, while highlighting the importance of controllability and robustness. All test results are available on https://github.com/yxyuanxiao/NanoBanana2TestOnIR.

Paper Structure

This paper contains 11 sections, 7 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Image Restoration
  4. Image Editing Models
  5. Experimental Setup
  6. Results and Analysis
  7. Impact of Prompt Design
  8. Performance Across Scenes and Degradations
  9. Comparison with State-of-the-Art Methods
  10. Limitation and Discussion
  11. Conclusion

Figures (7)

  • Figure 1: Qualitative comparison of prompt length. Long prompts yield more accurate and detailed restorations than short prompts, especially for challenging scenarios such as text and surveillance.
  • Figure 2: Effect of fidelity constraints. Prompts without fidelity introduce semantic artifacts, while fidelity-constrained prompts preserve structure and semantics.
  • Figure 3: Failure cases with fidelity constraints. Infidelity still occurs despite explicit fidelity guidance.
  • Figure 4: Failure cases in output stability. Repeated runs on the same input produce inconsistent results, including color shifts, scale variations, and structural changes.
  • Figure 5: User study results. Nano Banana 2 achieves the highest average score and shows more consistent perceptual quality.
  • ...and 2 more figures