> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/ai-blocks/super-resolution.md).

# Super Resolution

This function block enhances image quality by upscaling input images using pre-trained super-resolution models. Choose a model that fits your speed and quality needs, then feed an image to get an upscaled result.

## 📥 Inputs <a href="#inputs" id="inputs"></a>

`Image Any` This socket accepts the image you want to enhance.

## 📤 Outputs <a href="#outputs" id="outputs"></a>

`Image Any` The upscaled/enhanced image produced by the block.

## 🕹️ Controls <a href="#controls" id="controls"></a>

`SuperResolution Type` A dropdown to select the upscaling model and scale. Options present different speed vs. quality tradeoffs. Example options include `BEST_x2`, `MEDIUM_x4`, `FAST_x3`, and `FASTEST_x2`. Select the option that fits your hardware and latency requirements.

## 🎨 Features <a href="#features" id="features"></a>

* Upscaling with multiple model choices offering different quality and performance levels.
* Hardware acceleration support for faster processing when a compatible GPU is available.
* Simple one-input / one-output flow for easy insertion into existing pipelines.

## ⚙️ Running mechanism <a href="#how-it-works" id="how-it-works"></a>

* Choose a model from the `SuperResolution Type` control.
* When the block runs, it applies the selected upscaling model to the incoming image and outputs the enlarged image.
* Larger scale factors and higher-quality models will require more processing time and memory.

## 📝 Usage Instructions <a href="#usage" id="usage"></a>

1. Connect an image source to the `Image Any` input.
2. Use the `SuperResolution Type` dropdown to pick a model and scale (for example x2, x3, or x4).
3. Run your scenario to produce the upscaled image on the `Image Any` output.
4. Preview the result with a display or save it with an exporter block.

## 💡 Tips and Tricks <a href="#tips-and-tricks" id="tips-and-tricks"></a>

* If your input images are very large and processing is slow, use `Image Resizer` to reduce the image size before upscaling, or choose a lower scale model such as `FASTEST_x2`.
* To inspect the result visually, connect the output to `Show Image` so you can preview the enhanced image interactively.
* If you want to save processed images for later review, connect the output to `Image Logger` or `Image Write`.
* Apply super resolution only to important regions to save resources: crop with `Image ROI Select` or `Get ROI`, run super resolution on the cropped region, then merge back if needed.
* For downstream tasks that benefit from higher-resolution detail (small object recognition or text reading), try connecting the output to `Object Detection (D-FINE)`, `Object Detection`, `OCR (EasyOCR)`, or `OCR` to improve detection and recognition accuracy.
* If you need to maximize detection throughput, balancing quality and speed is key: prefer `MEDIUM_*` or `FAST_*` models when real-time performance is important.

## 🛠️ Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

* GPU out of memory or long processing times
  * Try a lighter option from the `SuperResolution Type` list such as `FASTEST_x2` or reduce the input image size with `Image Resizer`.
  * Close other GPU-intensive applications before running the pipeline.
* Output looks unchanged or artifacts appear
  * Try a different model/scale setting. Higher-quality models improve detail but may introduce different artifacts depending on image content.
  * Consider preprocessing with `Denoising`, `Blur` or `Image Resize` to improve input quality before upscaling.
* Slow evaluation on many images
  * Use `Batch Processing` to control memory use and throughput, or upscale only selected ROIs using `Image ROI Select`.

## 📊 Evaluation <a href="#evaluation" id="evaluation"></a>

On execution, the block produces a single upscaled image reflecting the chosen model and scale. Performance depends on model choice, image size, and available hardware.