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Generation Techniques

Super Resolution — What is it?

Technology for creating a high-resolution version from a low-resolution image using artificial intelligence. Similar to upscale but can also fill in non-original areas of the image through hallucination.

Detailed Explanation of Super Resolution

Super Resolution is one of the oldest and most active research areas in image processing. With the arrival of AI, the field has leaped far beyond classical interpolation methods -- models can now genuinely hallucinate new detail rather than merely averaging existing pixels.

Classical Upscaling vs. AI Super Resolution

Bilinear/Bicubic interpolation: Enlarges by averaging neighboring pixels. Results are smooth and blurry; no new detail is generated.

AI Super Resolution: During training, the model sees pairs of high- and low-resolution images, learning that this type of structure typically has this detail. At inference time, it uses that learned knowledge to hallucinate plausible, realistic detail.

Key AI Super Resolution Models

1. ESRGAN (Enhanced Super Resolution GAN): The cornerstone of the open-source world, with optimized weight sets for x2 and x4 upscaling.

2. Real-ESRGAN: ESRGAN optimized for real-world degradation -- particularly effective on images with compression artifacts, noise, and blur.

3. SwinIR: Vision Transformer-based approach that outperforms ESRGAN on several benchmarks.

4. SD Upscaler / Tile Diffusion: Stable Diffusion's own super-resolution approach, splitting the image into tiles and running diffusion on each -- producing genuinely new, coherent detail rather than just sharpening.

5. Topaz Gigapixel AI: The commercial industry standard, widely used by photographers and the film industry.

Common Use Cases

- Bringing small generated images to print quality - Refreshing old or web-resolution photographs - Upscaling individual video frames - Preparing high-resolution e-commerce product images

Hallucination Risk

AI super resolution's biggest limitation is the risk of generating plausible but incorrect detail. In forensic, documentary, and journalistic contexts, it is critical to note that AI-upscaled images are not authentic originals.

On tasarim.ai, Clipdrop's Image Upscaler and Adobe Firefly's upscale features both use super-resolution technology, enabling 2x or 4x enlargement suitable for print and social media.

Tip for beginners: Generate your AI images at low or medium resolution first (faster iteration), then upscale the best result 2x-4x with super resolution. This workflow optimizes both speed and final quality.

More Generation Techniques Terms

Text-to-Image

Technology that generates images from natural language text descriptions using artificial intelligence. The prompt written by the user is interpreted by the AI model and converted into an image.

Text-to-Video

Technology that generates video content from natural language text descriptions using artificial intelligence. It converts text prompts into moving, consistent frame sequences.

Image-to-Image

A technique that generates or transforms a new image using AI by referencing an existing image. The structure of the input image is preserved while style, content, or details can be modified.

Inpainting

A technique for regenerating or editing a specific area of an image by masking it with AI. Used for removing unwanted objects or modifying specific areas.

Outpainting

A technique that extends the boundaries of an existing image using AI. It enlarges the image by creating new areas that are consistent with the original content.

Upscaling

The process of enlarging low-resolution images using AI without quality loss or with quality enhancement. It differs from traditional resizing with its ability to add detail and sharpen.