A trustworthy AI photo enhancer should make it possible to inspect what changed, why it changed, and whether the result fits the intended use. Keep the original untouched, save an enhanced derivative, and record the model, settings, and output dimensions. Then assess the derivative against both the source and a clearly defined goal: an output with more pixels is not, by itself, proof of greater fidelity.
What should a verifiable enhancement show?
Enhancement can mean denoising, sharpening, enlarging an image while preserving detail, or generating plausible detail that was not clearly present in the input. These are different aims, so a single score or larger file cannot establish that an image has been improved. Adobe describes distinct goals for Photoshop Generative Upscale: Firefly is intended to restore low-resolution images, Topaz Gigapixel to preserve existing detail, and Topaz Bloom to add creative detail. Adobe’s Photoshop Generative Upscale documentation describes these model distinctions and current limits.
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A verifiable output is a saved derivative linked to its source and processing choices. A reviewer should be able to identify the input, the model and settings used, the output dimensions, and the task the enhancement was meant to accomplish. This is a product-design recommendation, not an established industry standard.
Decide what “better” means before processing
Choose a concrete task before selecting a model or evaluating a result. For example, the task might be reducing visible compression artifacts, preparing a small image for a particular print size, or improving perceived sharpness on screen. A model that reconstructs texture may be suitable for one goal and unacceptable for another if faithful preservation is required.
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- Preservation: retain recognizable source detail while enlarging or cleaning the image.
- Restoration: reduce defects or recover a usable appearance from a small or degraded input.
- Creative reconstruction: synthesize plausible texture or detail, accepting that the output may not represent information recoverable from the original.
For a source image, start with the original file rather than a compressed copy when possible. Topaz warns that its models depend on existing image quality and that very low-resolution inputs can produce unwanted artifacts. Its documentation also lists different model choices and controls, including Standard, High Fidelity, Low Resolution, Graphics, Standard MAX, and Recover v3. Recover v3 has specific cautions around text and logos, highly structured or high-grain images, and high-resolution images. See Topaz Photo’s processing options documentation.
Choose the model and scale for the source and use
Model labels and limits are vendor specifications, not independent evidence that one option produces better results. Compare choices by intended enhancement, input condition, desired scale and final dimensions, processing location, and required file format or workflow.
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| Option | Documented distinction | Constraints and workflow notes |
|---|---|---|
| Photoshop Generative Upscale | Firefly is described as restoring low-resolution images; Topaz Gigapixel as preserving detail; Topaz Bloom as adding creative detail. | Adobe documents 2x and 4x scaling. Its 2026 documentation lists Firefly Upscaler support up to 6144 × 6144 pixels, Topaz Gigapixel up to 56 megapixels, and Topaz Bloom up to 9 megapixels. These are vendor limits and can change. Adobe documentation. |
| Topaz Photo | Provides scale, dimensions, and resolution controls, with models for different image conditions and types. | Topaz describes enlargement up to 600% for eligible small images, capped at a 32,000-pixel long side or 2 GB file size, whichever is reached first. Model suitability depends on the input. Topaz documentation. |
| Topaz Gigapixel core models | Standard is general-purpose; High Fidelity targets higher-quality sources; Low Resolution targets compressed or small images; Text & Shapes targets typography and geometric assets. | Topaz labels these core models non-generative and locally processed. Topaz model documentation. |
| Lightroom Super Resolution | Offers 2x and is positioned by Adobe for RAW-first workflows without generative credits. | Choose it when a RAW-first workflow matters more than 4x scaling. Adobe Lightroom Enhance documentation. |
| Lightroom Generative Upscale with Topaz | Offers 2x and 4x, and returns a derivative DNG. | It consumes generative credits; Adobe recommends denoising first and using it near the end of editing because it creates a new file. Adobe’s 2026 FAQ lists a 65,000-pixel long-edge limit and 10 credits for outputs up to 25 megapixels or 20 credits above 25 megapixels. Credit use and limits are vendor specifications that may change. Adobe Lightroom FAQ. |
These options are not a matched benchmark. Without evaluating the same source images against a defined goal, it is not justified to declare one universally best.
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- Preserve the input. Store the original file unchanged and create the enhanced image as a separate derivative. Photoshop creates a new document for its upscale workflow, and Lightroom Generative Upscale returns a separate DNG. Photoshop documentation; Lightroom FAQ.
- Record the processing choices. Capture the model, version if available, scale, relevant settings, and output dimensions. These details let someone distinguish a preserving workflow from a generative one and reproduce or evaluate the result.
- Keep the intended task with the derivative. Note whether the output was made for artifact cleanup, a print-size enlargement, or perceived sharpness. A result cannot be judged consistently if the intended outcome is unknown.
- Make the relationship easy to inspect. Use a clear source-to-derivative association in the app or project record; do not overwrite the original or present an enhanced image as if it were the untouched source.
Compare the result with the original
Use quantitative checks alongside visual inspection, and judge the output at the size and context in which it will actually be viewed. Examine important regions such as faces, lettering, fine texture, edges, and smooth backgrounds. Look for artifacts, oversharpened outlines, invented texture, lost detail, or changes that make the image less suitable for its intended task.
Pixel-wise scores alone cannot settle whether an image looks natural or faithful. In their 2016 paper EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis, Mehdi S. M. Sajjadi, Bernhard Schölkopf, and Michael Hirsch explain that peak signal-to-noise ratio (PSNR) can correlate poorly with human perception of image quality; they also note that optimizing such metrics can yield oversmoothed images lacking natural high-frequency texture. The paper’s arXiv abstract is an example of why a numeric reconstruction score should not substitute for visual assessment.
For a non-generative workflow, check whether the output preserves the details that matter from the source. For a generative workflow, assess whether newly rendered detail is acceptable for the task, and make clear that it may be plausible rather than recovered. A convincing appearance does not prove that the added detail existed in the original.
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