Category: AI Technology, Post-Production

Definition: An AI technique that increases image resolution and detail beyond the original capture, using machine learning to intelligently predict and add pixel information. Transforms low-resolution product photos into high-quality images suitable for large-format use.

Why It Matters: Salvages low-resolution legacy content for modern use; enables cropping and zooming without quality loss; reduces need for reshoots of older products; improves mobile photos to professional standards; prepares images for print from web sources.

Use Cases: Upscaling older product photography, enhancing mobile-captured images, preparing web images for print catalogs, improving detail shots, recovering compressed images, creating billboard-sized imagery from standard photos.

Example of Real Use Case: A fashion brand wants to reprint a successful catalog from 2015, but the original images were only captured at web resolution. They use super-resolution AI to upscale 500 images to print quality, avoiding a complete reshoot worth $50,000.

Software/Service: Topaz Gigapixel AI, Adobe Photoshop Super Resolution, Let's Enhance, Upscayl, Real-ESRGAN, Magnific AI, Caimera AI

Common Issues: Artificial-looking details when over-upscaled, introduction of unwanted patterns or textures, inability to recover truly missing information, inconsistent results across different image types, processing time for large batches.

Do's and Don'ts:

• ✓ Do start with best available source quality

• ✓ Do test upscaling settings on sample images

• ✓ Do verify fabric textures remain realistic

• ✓ Do maintain reasonable resolution expectations

• ✗ Don't upscale beyond 4x original resolution

• ✗ Don't use super-resolution to fix poor photography

• ✗ Don't skip manual review of upscaled images

Related Terms: AI Retouching, Image Compression, RAW Format, Detail Shot, Post-Production, Image Upscaling

Also Known As: AI Upscaling, Image Enhancement, Resolution Enhancement