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

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

📤 Outputs

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

🕹️ Controls

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

• 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

• 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

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

• 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

• 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

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.