Use traditional resizing when you need predictable pixels, clean hard edges, or faithful documentation. Use AI upscaling when a photograph must become substantially larger and you accept that the extra texture is a model’s plausible estimate, not verified recovery of what the camera captured. Both methods build a larger pixel grid. The difference is how they estimate the new pixels, and that difference affects fidelity, sharpness, artifacts, and whether the result is suitable for evidence or archival work.
For an important image, process a copy, compare a conventional result with an AI result at the final screen or print size, inspect faces, text, edges and repeated patterns, and keep the untouched original. There is no universal winner across image types or current software.
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What changes when you enlarge an image?
Changing pixel dimensions is commonly called resampling. Adobe’s Photoshop Help defines it this way: “Resampling changes an image’s pixel dimensions during resizing or transformation.” When you enlarge a file, the software has to fill a bigger grid with values that were not explicitly present in the source. When you reduce it, several source pixels are combined into fewer output pixels.
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That distinction matters because a low-resolution image has already lost high-frequency information such as fine hair, tiny lettering and texture. The single-image super-resolution problem is therefore ill-posed: more than one high-resolution image can be consistent with the same low-resolution input. Shi and colleagues describe this one-to-many ambiguity in their technical paper on single-image super-resolution (arXiv, 2016).
Traditional resizing: predictable interpolation
Conventional resampling is deterministic. Given the same source, dimensions and algorithm, it calculates the same result without inventing a learned interpretation of the scene. That makes it easier to explain, reproduce and audit.
Nearest-neighbor
Nearest-neighbor assigns each new pixel the value of the closest source pixel. It can look blocky on photographs, but that behavior is exactly why it works for pixel art, screenshots with deliberate single-pixel geometry, icons and masks. It preserves hard boundaries instead of creating blended colors between them.
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Bilinear interpolation averages the surrounding pixels. Transitions are smoother than nearest-neighbor, but fine detail can look soft. It is useful for quick, modest changes when speed matters more than maximum edge definition.
Bicubic methods
Bicubic algorithms use a broader neighborhood and a smoother mathematical curve. Photoshop labels Bicubic Smoother as an enlargement option and Bicubic Sharper as a reduction option. They are a practical baseline for ordinary photographs because gradients usually remain smooth without the blockiness of nearest-neighbor.
Preserve Details 2.0
Photoshop’s Preserve Details 2.0 is an enlargement method designed to retain edges and fine detail while limiting artifacts. It also provides a noise-reduction control. Adobe’s explanations of these choices are in its resampling options documentation and its guide to increasing image resolution.
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Because these methods derive values from the existing pixels, they generally avoid adding recognizable objects or textures that were never represented in the source. The trade-off is that a very small or blurred photo usually remains visibly soft after enlargement.
AI upscaling: a plausible reconstruction
AI upscaling, also called single-image super-resolution, applies a neural model trained to map low-resolution image patches to high-resolution examples. Adobe says its Super Resolution model was trained on millions of low- and high-resolution patch pairs (Adobe, 2021). The model can recognize patterns associated with eyelashes, feathers, brick, foliage or fabric and synthesize a visually convincing version at a larger size.
“Convincing” is not the same as “recovered.” If a face contains only a few blurred pixels, the model cannot know the person’s exact iris pattern or the original shape of every hair. It chooses a likely explanation based on learned patterns. Treat those pixels as an interpretation, especially when the image could be used for identification, evidence or historical record.
Why results vary with the source
AI models depend on the information and degradation present in the input. Topaz’s documentation warns that extremely low-resolution or poor-quality files can lead to unwanted artifacts and recommends starting with the original rather than a compressed copy when possible (Topaz Photo documentation).
Real photographs also contain sensor noise, sharpening halos, demosaicing patterns, motion blur and JPEG blocking. A model trained mostly on clean, synthetic reductions may not generalize to that combination. Lugmayr, Danelljan and Timofte discuss this domain gap—especially the failure of models trained on synthetic bicubic degradation to handle natural noise and compression—in their real-world super-resolution paper (arXiv, 2019).
Quick decision guide
| Situation | Start with | Inspect before keeping the result |
|---|---|---|
| Pixel art, icons or deliberately hard-edged graphics | Nearest-neighbor or another conventional edge-preserving method | Block boundaries, one-pixel lines and intentional color regions |
| Modest enlargement of a normal photograph | Compare Bicubic or Preserve Details 2.0 with an AI result | Gradient smoothness, halos, noise and whether texture looks natural |
| Very small, compressed photo that must be larger | Try AI, but retain a conventional baseline | Faces, lettering, repeating patterns, compression blocks and invented edges |
| Documentary, evidentiary, archival or identity-sensitive use | Find the original, a fresh capture or a higher-quality scan first | Whether the processed file is clearly labeled and the untouched source is preserved |
| Print production | Set the physical size and required pixel dimensions, then compare methods | A proof viewed at the intended distance, not only a zoomed-in screen view |
The most useful comparison axes are source fidelity versus perceptual sharpness, image type, the kind and severity of degradation, enlargement factor, important content in the frame, available processing time and the consequences of a wrong detail.
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How to choose by image type
Pixel art, logos and hard-edged graphics
Choose nearest-neighbor when the source intentionally uses discrete blocks. Smoother interpolation blends adjacent colors and can create muddy outlines or unintended intermediate colors. If the graphic contains both pixel-art regions and smooth photographic elements, resize those regions separately or use masks so the hard edges receive the appropriate treatment.
Everyday photographs
Run a conventional enlargement first. If it is clean but too soft at the final output size, compare an AI version. Look at the whole image at normal viewing distance as well as 100% magnification. A texture that appears impressive when zoomed in may be distracting or false when the image is viewed normally.
Faces and people
Examine eyes, teeth, hairlines, ears and jewelry at full output size. AI can produce asymmetrical pupils, waxy skin, repeated eyelashes or a plausible but incorrect tooth edge. A conventional result may look less sharp yet remain more faithful to the limited evidence in the source.
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Text, signs and screenshots
Neither method can reliably reconstruct characters that are absent or unreadable in the source. AI may turn noise into letter-like strokes that look authoritative. If the wording matters, locate the original document or capture a higher-resolution image. For interface screenshots and diagrams, conventional methods usually preserve straight lines and exact glyph shapes better.
Old prints, scans and compressed downloads
Search for the negative, original scan or highest-quality file before enlarging. Remove avoidable JPEG recompression from the workflow. If an AI model is used, check for repeating textures, plastic-looking surfaces and halos around contrasty edges; these are common places for a model to impose a pattern rather than recover one.
A reliable comparison workflow
- Preserve the source. Make a working copy and retain the original file, metadata and any earlier edits. Do not overwrite the only high-quality capture.
- Define the output first. Record the required pixel width and height for the destination screen, layout or print. If the destination is a print, decide the physical dimensions and viewing distance before choosing a pixel count.
- Crop consistently. Make the same crop for every test. A different crop changes the apparent detail and makes the comparison meaningless.
- Create a conventional baseline. In Photoshop, open the image’s resize controls, enable resampling, enter the target dimensions and compare Bicubic Smoother, Bicubic Sharper where appropriate, and Preserve Details 2.0. Keep each result as a separate file.
- Create the AI version. Use the super-resolution or upscale control in your chosen application. Record the scale and any face, noise or sharpening settings so the result can be reproduced.
- Compare at two scales. View the complete image at its intended display or print size, then inspect 100% crops of important areas. Include a smooth gradient, a high-contrast edge, a face or text area, fine texture and any repeating pattern.
- Check for invented structure. Look for halos, doubled lines, checkerboard or honeycomb textures, melted details, repeated marks, incorrect letters and unnatural skin. An apparently sharper edge is not automatically a more accurate edge.
- Choose and document. Keep the original and the selected derivative. Note the application, model or algorithm, scale, output dimensions and date in the derivative’s metadata or project notes.
Output size, PPI and print decisions
Choose pixel dimensions for the actual destination rather than enlarging indefinitely. A larger file can consume more storage and processing time without adding useful information.
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PPI (pixels per inch) describes how many image pixels are placed along an inch of a print. DPI describes the printer’s output dots and is not the same measurement. A high PPI number entered into metadata does not create newly captured detail; it changes how existing pixels are assigned to a physical size.
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Viewing distance changes what density is practical. Topaz gives these vendor examples: about 300 PPI for close viewing, 240–120 PPI when a print is viewed across a room, and 90–30 PPI when viewed across a street (Topaz Gigapixel documentation). They are examples rather than a universal print standard. Make a proof at the intended size and distance, because the final viewing condition matters more than inspecting an oversized file at 400%.
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Adobe’s Super Resolution feature article illustrates a one-button enlargement from 10 megapixels to 40 megapixels and describes enlarging a cropped bird photograph from 2.5 megapixels to 10 megapixels for a larger print (Adobe, 2021). These are feature examples, not independent benchmarks or promises that every image gains four times as much real detail.
Topaz documents a maximum scale factor of up to 6× and a longest-side limit of 32,000 pixels for its Gigapixel software (Topaz documentation). Those are documented product limits, not recommendations for the quality or usefulness of a particular enlargement. Your computer, source dimensions and destination may impose additional practical limits.
Common failure modes and fixes
The AI result looks sharp but wrong
Return to the source and compare the same crop. Reduce the scale, lower face or detail enhancement, or use the conventional version. If the image is evidence or identification material, do not use a synthesized feature as proof of what was present.
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Halos can come from aggressive sharpening or a model’s edge prior. Try a less aggressive setting, a different conventional algorithm, or a smaller enlargement followed by restrained sharpening. Inspect lettering and high-contrast branches first because halos are easiest to see there.
Noise and JPEG blocks become texture
Use the original file, not a messaging-service download or repeatedly exported JPEG. Test noise reduction separately from enlargement. If a model turns blocks into repeating detail, keep the cleaner conventional result or repair the area from a higher-quality source.
The output is still blurry
Upscaling cannot reverse severe motion blur, defocus or information that was never sampled. A larger pixel grid may make the blur easier to print but does not restore the missing scene. Seek a new capture, original negative or better scan when the content is important.
The file is too large to process or deliver
Resize to the destination dimensions rather than the maximum available scale. Crop unused areas first, use a sensible bit depth and export a delivery copy while retaining a high-quality master. Topaz’s 32,000-pixel longest-side limit applies to the documented Gigapixel software, not to every application.
Authenticity, privacy and responsible use
For a family print or creative illustration, plausible detail may be an acceptable aesthetic choice. For journalism, legal evidence, insurance claims, scientific records, historical archives or identity decisions, the priority is traceability. Keep the unaltered source, make the processed derivative visibly identifiable, and record the method and settings. If a detail cannot be verified from the original or an independent source, describe it as an enhancement rather than a recovered fact.
Do not use an AI enlargement to manufacture readable text, identify an unknown person or assert that a barely visible object definitely exists. The mathematical ambiguity described in the super-resolution literature means a visually persuasive output can still be factually wrong.
Which software approach fits?
Adobe Photoshop and compatible Camera Raw workflows provide conventional resampling choices and Adobe’s Super Resolution option. The relevant controls are useful when you want one application to handle cropping, color, output dimensions and enlargement. Adobe’s published examples explain the feature, but they are not a head-to-head test against every other product.
Topaz’s Gigapixel documentation describes AI enlargement controls, a documented up-to-6× scale factor and a 32,000-pixel longest-side limit. Topaz Photo documents AI upscaling and warns that poor inputs can produce artifacts. These vendor pages explain capabilities and limitations; they do not establish a universal winner. Select software by the controls you need, local processing and privacy requirements, supported file types, repeatability and whether its output can be reviewed before delivery.
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The practical verdict
Start with traditional resampling for pixel art, interface graphics, exact diagrams, modest changes and any workflow where predictable source fidelity is more important than apparent detail. Test AI super-resolution for photographs that must become substantially larger, especially when a conventional result is clearly too soft—but treat every new texture as a model-generated estimate. The defensible choice is the version that looks natural at the intended size, introduces the fewest artifacts in important areas and matches the image’s required level of authenticity.
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