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Build an AI photo enhancer around a saved, inspectable derivative—not a vague “improved” label. Keep the original, identify the model and settings used, state whether the model preserves or reconstructs detail, and let users compare the result against both the source and the task they intended.
What makes an enhanced photo verifiable?
A verifiable output is a derivative image whose relationship to its input can be checked. A user should be able to identify the original, the selected model and settings, the output dimensions, and the intended task—for example, reducing compression artifacts or preparing a small image for a larger print.
This matters because “enhancement” can mean different things. A model may denoise, sharpen, enlarge while preserving existing detail, or generate plausible detail that was not resolved in the source. Adobe distinguishes these aims in its Photoshop Generative Upscale documentation: Firefly is described as restoring low-resolution images, Topaz Gigapixel as preserving existing detail, and Topaz Bloom as adding creative detail. A larger output is not, by itself, evidence that the added pixels faithfully represent the original scene.
How should the enhancer preserve the original and document its output?
Keep the source unchanged
Do not overwrite the input. Save an enhancement as a separate derivative and make the source available for comparison. Adobe describes Photoshop Generative Upscale as creating a new document, while Lightroom’s generative upscale produces a separate DNG file. These workflows preserve a visible distinction between input and output; the same principle is useful in a product even when its file format or interface differs.
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Record enough information to reproduce the choice
For each derivative, retain the model name and version when available, scale factor, relevant settings, output dimensions, and the selected task or goal. This is a product-design recommendation, not an industry standard. The record makes a comparison meaningful: “4×” alone does not identify whether a model was intended to preserve detail, restore a small image, or synthesize texture.
Label generative reconstruction honestly
When a model can add detail, say so before or alongside processing. A generated texture can look convincing without being recoverable from the original. Make the distinction visible in the output record and comparison view rather than presenting every enlarged image as a faithful recovery.
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How can users tell whether an enhancer improved the photo?
First define “improved” for the image at hand. The right outcome depends on whether the goal is to remove compression artifacts, prepare a print-size enlargement, make a subject appear sharper, or preserve a source as faithfully as possible. A model suited to one goal may not serve another.
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- Compare source and derivative side by side. Provide a clear before-and-after view, with a way to inspect the same region at the same zoom level.
- Inspect consequential regions at the intended viewing size. Check faces, text, fine texture, edges, and backgrounds for invented patterns, halos, smearing, or other unwanted changes. This is practical evaluation guidance, not a universal standard.
- Use metrics as supporting evidence. Quantitative measures can help detect pixel-level differences, but they cannot settle whether an image looks natural or meets the user’s purpose.
In their 2016 paper EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis, Mehdi S. M. Sajjadi, Bernhard Schölkopf, and Michael Hirsch note that pixel-wise measures such as peak signal-to-noise ratio (PSNR) can correlate poorly with human perception. They also explain that optimizing for such measures can produce oversmoothed images that lack high-frequency texture and look unnatural despite a high PSNR. Treat PSNR as one signal, not a verdict on perceived quality or authenticity.
Can AI upscaling add details that were not in the original?
Yes. Some approaches aim to preserve existing detail; others generate plausible new detail. That distinction is explicit in Adobe’s descriptions of Photoshop’s Firefly, Topaz Gigapixel, and Topaz Bloom options. When detail is generated, the output should not be represented as proof that the source contained that exact texture or feature.
Input quality also affects the result. Topaz warns that its models depend on the quality of the image and that very low-resolution inputs can produce unwanted artifacts. Use the original file rather than a compressed copy where possible. No output scale can recover information that was never captured with certainty.
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How should an enhancer handle scale and output limits?
Scale choices affect more than the number of pixels: they determine file size, workflow fit, and which processing options are available. The following are vendor specifications, not independent performance findings, and may change.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Workflow or option | Documented scale or limit | What to account for |
|---|---|---|
| Photoshop Generative Upscale | Adobe documents 2× and 4× scaling. Its Firefly Upscaler supports images up to 6144 × 6144 pixels; the listed Topaz Gigapixel option supports up to 56 megapixels, and Topaz Bloom up to 9 megapixels. | Model limits differ, so check the selected option rather than assuming one shared maximum. Adobe documentation, 2026. |
| Topaz Photo | Topaz documents enlargement up to 600% for eligible small images, with a maximum long side of 32,000 pixels or a 2 GB file size, whichever is reached first. | Available models include Standard, High Fidelity, Low Resolution, Graphics, Standard MAX, and Recover v3; fit depends on source quality and image type. Topaz documentation, publication date not stated. |
| Lightroom Super Resolution | Adobe documents 2× upscaling. | Positioned for RAW-first work without generative credits. Adobe documentation. |
| Lightroom Generative Upscale with Topaz | Adobe documents 2× and 4× options and a 65,000-pixel long-edge limit. | Produces a derivative DNG, uses generative credits, and Adobe recommends denoising first and using the feature near the end of editing. Adobe documentation, 2026. |
For Lightroom Generative Upscale with Topaz, Adobe’s FAQ lists 10 credits for Topaz Gigapixel outputs up to 25 megapixels and 20 credits for outputs above 25 megapixels. These are the credit amounts in the FAQ last updated February 20, 2026; check the current documentation for changes. Adobe’s FAQ.
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How should users choose a model or workflow?
Compare the available choices against the task, not a blanket claim that one model is best. The documentation supports differences in stated purpose and constraints, not a matched performance ranking.
- For preserving detail in a small or compressed image: Topaz describes Gigapixel’s core models as non-generative and locally processed. Standard is general purpose, High Fidelity targets higher-quality sources, Low Resolution targets compressed or small images, and Text & Shapes targets typography and geometric assets. Topaz model documentation.
- For a RAW-first workflow at 2×: Lightroom Super Resolution is Adobe’s non-generative-credit option. It may be a better workflow fit when RAW handling matters more than 4× scaling. Adobe documentation.
- For a choice between restoration, preservation, and creative detail: Photoshop Generative Upscale offers Adobe-described model distinctions and 2× or 4× scaling, subject to model-specific input limits. Adobe documentation.
- For detailed control over scale, dimensions, and resolution: Topaz Photo provides those controls and several model choices. Its Recover v3 guidance cautions against text and logos, highly structured or high-grain images, and high-resolution images. Topaz documentation.
A useful selection screen should make the trade-off legible: intended goal, source condition, whether detail may be generated, target scale and dimensions, processing location when known, and the output file type. Do not rank options as universally superior without a matched test set and an explicit evaluation method.
What should a product communicate about evaluation?
Show what the output is and how it was made, then let users judge it against the original and their stated goal. Distinguish pixel-level metrics from human assessment, and do not imply that a high score or higher resolution proves naturalness or fidelity. No universal visual checklist or single metric settles every use case; faces, text, textures, and other consequential details require task-specific inspection.
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