How to Increase Image Resolution Without Losing Quality

Every image has a fixed number of pixels. When you need it larger, for a print, a banner or a zoomable product page, software has to invent the pixels in between. Whether the result looks crisp or mushy depends almost entirely on how those pixels are invented and on what you do before and after.

This guide walks through a workflow that works with any decent upscaler, including the one we are building at IncreaseAI.

First, be clear about what “resolution” means

People use “resolution” for three different things:

• Pixel dimensions, such as 1200×800. This is the only number that describes how much detail a file can hold.
• Pixel density (PPI), which only matters when the image is printed or shown at a physical size.
• Perceived sharpness, meaning how much real detail and clean edges the image has.

Increasing pixel dimensions is easy. Increasing perceived sharpness at the same time is the hard part. If you only change the PPI number in an editor’s “Image Size” dialog without resampling, nothing about the image changes except the print size it claims. We cover that in depth in DPI vs PPI vs pixel dimensions.

Step 1: Start from the best source you have

Upscaling magnifies whatever is in the file, including damage. Before anything else:

• Find the original. An image downloaded from social media or a messaging app has usually been resized and recompressed, sometimes several times. The camera original or the first export is always a better starting point.
• Avoid screenshots of images. A screenshot adds another resampling step and often a scaled display.
• Do not upscale an image that was already upscaled. Stacking two enlargements compounds artifacts. Go back to the smaller original and do a single larger step.

Step 2: Clean up noise and compression artifacts first

Two kinds of damage get worse when enlarged:

1. Sensor noise, the fine grain in low-light photos.
2. JPEG artifacts, the blocky 8×8 patterns and ringing around edges caused by heavy compression.

If you upscale first, the algorithm treats that noise as detail and dutifully enlarges it. Removing it first gives the upscaler a cleaner signal. Many AI upscalers, including the design we are working toward, include a denoise step that runs before enlargement for exactly this reason.

Keep denoising moderate. Aggressive noise reduction smears skin, hair and fabric into a plastic look, and no upscaler can bring that texture back.

Step 3: Choose the right scale factor

Work out the pixel size you actually need, then pick the smallest factor that gets you there.

You have	You need	Factor
1000×667	2000 px wide for a web hero	2×
1000×1500	A 10×15 in print at 300 PPI (3000×4500)	3×, so use 4× and downscale
800×800	2000×2000 for a marketplace listing	2.5×, so use 4× and downscale

When the factor you need falls between options, upscale to the next level up and then downscale to the exact target with a good resampling filter. Downscaling is lossless in spirit: it throws away pixels rather than inventing them, and it slightly increases apparent sharpness.

Going beyond 4× in one go rarely pays off. At 8× the model is inventing 63 out of every 64 pixels, and the result tends to look painted.

Step 4: Pick the right method

There are two broad families:

• Interpolation (bicubic, Lanczos and friends) calculates new pixels as weighted averages of their neighbours. It is fast, predictable and never invents content, but edges go soft and blocky at large factors.
• AI super-resolution uses a neural network trained on pairs of low- and high-resolution images to predict what the missing detail probably looked like. Edges stay crisp and textures look natural, at the cost of occasionally inventing something that was not there.

For a 1.5× enlargement of a clean photo, a Lanczos resize is often fine. For 2× and above, or for anything soft or compressed, AI upscaling usually wins clearly. We explain the trade-offs in detail in AI upscaling vs bicubic and Lanczos.

Also match the model to the content. Models trained on photos tend to add fake texture to flat-colour artwork, and models trained on anime tend to flatten real skin. See our guide to upscaling anime and illustration for the artwork side.

Step 5: Inspect at 100%, not “fit to screen”

A scaled-down preview hides almost every problem. Zoom to 100% (one image pixel per screen pixel) and check:

• Faces and text. These are where AI upscalers are most likely to invent something wrong: odd teeth, changed eyes, garbled letters.
• Repeating textures. Brick, fabric and foliage sometimes turn into a uniform pattern.
• Edges against the sky. Look for halos (light outlines) from over-sharpening.
• Gradients. Smooth skies can show banding, especially if the source was an 8-bit JPEG.

If faces look wrong, try a lower factor, a lighter denoise setting, or a different model before reaching for manual retouching.

Step 6: Sharpen last, and lightly

Sharpening is the final step, after the image is at its output size, because the right amount depends on the final size and medium:

• For screens, a small amount of unsharp mask or a dedicated output sharpening control is enough.
• For print, you can sharpen a little more than looks ideal on screen, since ink spread on paper softens the result.

Stop as soon as you see halos around edges. Over-sharpening is the most common reason upscaled images look “AI-ish”.

Step 7: Export in a format that does not undo your work

After all that effort, saving as a low-quality JPEG will reintroduce block artifacts. A rough guide:

• PNG for a lossless master copy or for artwork with flat colours and text.
• High-quality JPEG (quality 85–92) for photos going to print services or general sharing.
• WebP or AVIF for images on your own website, where smaller files load faster.

The details, including when each format breaks down, are in JPEG vs PNG vs WebP vs AVIF.

A quick checklist

1. Use the earliest, least-compressed version of the image.
2. Denoise and remove compression artifacts, gently.
3. Upscale with the smallest factor that reaches your target, then downscale to exact size if needed.
4. Use an AI model suited to the content for 2× and above.
5. Inspect at 100%, especially faces, text and edges.
6. Sharpen once, at final size, for the final medium.
7. Export losslessly for masters and with sensible quality for delivery.

What upscaling cannot do

No method recovers information that was never captured. A face that is six pixels wide in the original will come out as a plausible face, not that person’s face. Text that is unreadable in the source may come out as convincing but wrong letters. For family photos and product images that is usually fine; for anything where accuracy matters, treat the enlarged image as an interpretation rather than evidence.

Within those limits, a careful workflow gets remarkably good results. IncreaseAI is being built to make steps 2 through 6 a single, predictable operation, with a free tier planned for small images.