Comprehensive Evaluation of Nano Banana Pro Based on 14 Tasks and 40 Datasets
- Pages: 1-36(2026)
Received:13 January 2026,
Revised:2026-05-02,
Accepted:07 May 2026,
Online First:07 May 2026
DOI: 10.11834/jig.260029
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Received:13 January 2026,
Revised:2026-05-02,
Accepted:07 May 2026,
Online First:07 May 2026,
移动端阅览
文本到图像生成模型的快速发展彻底改变了视觉内容创作。虽然诸如Nano Banana Pro之类的商业产品已获得广泛关注,但其作为传统底层视觉任务通用解决方案的潜力仍未得到充分探索。本文致力于解答一个核心问题:Nano Banana Pro是否是底层视觉全能选手?通过零样本评估的方式,在涵盖40个多样化数据集的14个底层视觉任务上进行了全面测试。仅使用简单文本提示而未进行微调的情况下,将Nano Banana Pro与最先进的专用模型进行对比。深入分析揭示了明显的性能分野:尽管Nano Banana Pro展现出卓越的主观视觉质量,其“幻觉生成”的高频细节常超越专用模型,但在传统基于参考的定量指标上表现欠佳。本文将这种差异归因于生成模型固有的随机性,即难以满足传统指标对像素级一致性的严苛要求。本文肯定了Nano Banana Pro作为底层视觉任务零样本解决方案的潜力,同时指出要达到领域专用模型的高保真度仍面临重大挑战。
The rapid evolution of large-scale text-to-image generation models has fundamentally transformed the landscape of visual content creation. Driven by advances in diffusion models, large multimodal pretraining, and scalable inference pipelines, modern generative systems have demonstrated unprecedented capabilities in synthesizing visually compelling images across a wide range of styles, scenes, and semantic conditions. Commercial models such as Nano Banana Pro have attracted significant attention due to their strong zero-shot generation ability, robust semantic understanding, and impressive perceptual quality. However, despite their success in creative image synthesis, a critical and largely underexplored question remains: Can such foundation generative models serve as general-purpose solvers for traditional low-level vision tasks? Low-level vision tasks—including dehazing, deblurring, super-resolution and so on—have historically been dominated by task-specific, regression-based models. These models are typically trained under strong supervision with paired data and optimized using pixel-aligned objectives such as PSNR(peak signal-to-noise ratio)and SSIM(structural similarity index measure). While highly effective within their target domains, such specialist models lack flexibility, often require costly retraining for new tasks, and struggle to generalize beyond their training distributions. In contrast, foundation generative models promise a unified alternative: a single pretrained model capable of addressing diverse vision tasks through natural language prompts, without task-specific fine-tuning. In this work, we present the first large-scale, systematic zero-shot evaluation of Nano Banana Pro across a broad spectrum of low-level vision tasks. Specifically, we investigate whether Nano Banana Pro can function as a low-level vision all-rounder—a generalist model capable of producing high-quality results across heterogeneous restoration, enhancement, and fusion tasks. To this end, we conduct an extensive evaluation covering 14 distinct low-level vision tasks across 40 datasets, encompassing both synthetic and real-world degradations. The evaluated tasks include deblurring (motion, defocus), super-resolution, image denoising, deraining, shadow removal, reflection removal, flare removal, low-light image enhancement, underwater image enhancement, HDR(high dynamic range)reconstruction, multi-focus image fusion, and infrared–visible image fusion, among others. All experiments are conducted under a standard zero-shot protocol. Nano Banana Pro is queried exclusively through simple, task-oriented natural language prompts, without any model fine-tuning, parameter adaptation, or task-specific post-processing. This setting is deliberately chosen to reflect realistic deployment scenarios and to assess the intrinsic capability of the model as a foundation visual system. For each task, we compare Nano Banana Pro against state-of-the-art specialist methods specifically designed for the corresponding task. Our comprehensive evaluation reveals a consistent and striking performance dichotomy. On one hand, Nano Banana Pro frequently produces results with superior perceptual quality, characterized by enhanced clarity, vivid textures, improved contrast, and visually pleasing color distributions. In many challenging scenarios—such as severe noise, extreme low-light conditions, heavy underwater color distortion, or strong atmospheric degradation—the model is able to hallucinate plausible high-frequency details and recover semantically coherent structures that rival or even surpass those generated by domain-specific methods. Across multiple tasks, Nano Banana Pro achieves competitive or leading performance on no-reference perceptual metrics and consistently receives favorable qualitative assessments. On the other hand, when evaluated using traditional full-reference, pixel-aligned quantitative metrics, Nano Banana Pro systematically underperforms compared to specialist models. Metrics such as PSNR, SSIM, SCD(sum of correlations of differences), and VIF(visual information fidelity) consistently reveal notable gaps, particularly in tasks requiring strict structural alignment or physical signal fidelity. This discrepancy is especially pronounced in tasks like denoising, HDR reconstruction, and image fusion, where pixel-level consistency with the reference image is heavily rewarded. We attribute this behavior to the inherent stochastic and generative nature of diffusion-based models, which prioritize semantic plausibility and perceptual realism over deterministic pixel correspondence. As a result, even visually improved outputs may be penalized for global color shifts, localized texture synthesis, or subtle geometric deviations. Importantly, our analysis shows that these quantitative penalties do not necessarily indicate failure. In many datasets, the provided “ground-truth” images themselves contain residual noise, blur, or imperfect color balance. In such cases, Nano Banana Pro often generates cleaner, more visually appealing results that deviate from the reference but align better with human perception. This observation highlights a fundamental tension between regression-based evaluation paradigms and generative reconstruction behaviors, and suggests that current benchmarks may be insufficient for assessing foundation generative models. Beyond aggregate metrics, we conduct detailed task-wise and dataset-wise analyses to characterize the operational scope and limitations of Nano Banana Pro. The model excels in scenarios involving severe degradation, ambiguous structure, or incomplete information, where its strong semantic priors can compensate for missing signal. Conversely, it struggles in applications demanding strict physical accuracy, such as forensic analysis, scientific imaging, or safety-critical perception, where hallucinated details or slight structural inconsistencies may be unacceptable. Collectively, our findings position Nano Banana Pro as a powerful zero-shot contender for low-level vision, capable of delivering high perceptual quality across a remarkably diverse set of tasks without retraining. At the same time, achieving the pixel-level fidelity of domain specialists remains a significant challenge. Rather than framing this as a binary competition between generative and regression paradigms, our results suggest a more promising direction: strategic integration. Future robust vision systems may combine the semantic imagination of foundation generative models with the physical constraints and precision of task-specific networks, leveraging the strengths of both. In summary, this study provides the first comprehensive empirical answer to the question: Is Nano Banana Pro a low-level vision all-rounder? Our answer is nuanced. Nano Banana Pro substantially raises the upper bound of perceptual quality in zero-shot low-level vision, but has yet to establish a stable lower bound suitable for high-fidelity, safety-critical applications. By systematically documenting these strengths and limitations across 14 tasks and 40 datasets, this report offers a detailed reference point for future research on foundation models in low-level vision, and calls for the development of new evaluation frameworks that better reflect perceptual realism, semantic consistency, and downstream utility.
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