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The problem is not simply that an algorithm touched the image. It is that a convincing upscale can make invented detail look like recovered fact. AI restoration can sharpen edges and synthesize texture, but when the original image does not contain enough information to determine a feature, a realistic result is not proof that the feature was there.
Why a sharper upscale can still be wrong
A low-resolution image leaves uncertainty: a small patch might represent hair, fabric, foliage, lettering, or something else. An AI restoration model uses patterns learned from other images to produce a plausible high-resolution version. That can make the output more coherent and photographic, but plausibility is not the same as fidelity to the specific source.
The risk is inherent to reconstructing information that the input does not preserve. The ESRGAN paper notes that hallucinated details can accompany artifacts, while an IEEE 2024 paper on diffusion-based image restoration describes hallucination as a concern for methods using deep generative priors. In this context, “hallucination” means a plausible-looking feature that differs semantically from the input or underlying scene—not just an obvious glitch. ESRGAN paper; IEEE MLSP 2024 paper.
What makes the result a problem
Generated detail can be mistaken for recovered detail
When the output looks crisp, viewers may infer that the model has revealed what the blurry source always contained. But a model can fill gaps with learned visual patterns. The added texture may be believable without being verifiable from the original.
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Looks-good and is-correct are different judgments
Detail, sharpness, semantic correctness, and overall quality should be assessed separately. A 2026 CVPR study treats these as distinct evaluation dimensions: an image can score well on visible detail or perceived quality while remaining incorrect about a depicted feature. CVPR 2026 study.
The intended use changes the standard
For a personal restoration or creative edit, a plausible interpretation may be acceptable if it is understood as an interpretation. In workflows where data consistency matters, however, invented detail can undermine the result. An IEEE paper on diffusion-based restoration identifies hallucination risk as a limitation in such applications; this does not establish a universal rule for every use or a particular legal evidentiary standard. IEEE MLSP 2024 paper.
How to judge an AI-upscaled image
- Keep the source. Preserve the original low-resolution image and treat the upscale as a derived version, not a replacement.
- Compare the same areas. Look at the source and output side by side, especially faces, text, distinctive objects, and small patterns. Ask whether the output clarifies a visible structure or supplies a new one.
- Separate four questions.
- Detail: Is more texture visible?
- Sharpness: Are edges clearer?
- Semantic correctness: Do the features remain consistent with what the source supports?
- Overall quality: Is the result coherent and useful for its purpose?
- Label uncertainty. If the upscale is shared or used in a consequential context, describe it as AI-restored or AI-upscaled rather than implying that every visible feature was recovered from the original.
What the evidence does—and doesn’t—show
Research establishes that generative restoration can produce false but realistic detail; it does not establish that every model or image fails in the same way. A SIAM Review article discusses hallucinations, instability, and unpredictable generalization across inverse problems, underscoring that outcomes depend on the method and task. An ISPRS Archives paper raises the narrower concern of nonexistent detail in a photogrammetry workflow, which should not be generalized to every image use. SIAM Review article; ISPRS Archives paper.
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One measure of model scale is not a measure of output truth. SUPIR’s 2024 CVPR paper reports training on 20 million high-resolution, high-quality images enriched with descriptive text annotations. That figure describes the training dataset; it is neither a hallucination rate nor an accuracy guarantee for a particular upscale. SUPIR CVPR 2024 paper.
There is no reliable statistic established here for how often consumer upscalers invent details or how often viewers mistake those details for genuine ones. The useful conclusion is narrower: judge an upscale by both appearance and fidelity, and do not treat added detail as evidence unless the source supports it.
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