

Instructir
Overview :
InstructIR accepts images and human-written instructions as input, performing integrated image restoration through a single neural model. It has achieved state-of-the-art results across multiple restoration tasks, including image denoising, rain removal, defogging, deblurring, and low-light image enhancement. ?? Get started with the demonstration tutorial. Visit our GitHub for more information.
Disclaimer: Please note that this is not a product, and you may notice certain limitations. This demonstration requires input of images with certain degradations (blur, noise, rain, low light, fog) and a prompt indicating the operation to be performed. The application may crash if input high-resolution images (2K, 4K) are used due to GPU memory limitations. The model is primarily trained on synthetic data, which may result in suboptimal performance on real-world complex images. However, it performs surprisingly well on real-world foggy and low-light images. You can also try general image enhancement prompts (e.g., 'polish this image', 'enhance color') to see how it improves color clarity.
Target Users :
By inputting images and human-written instructions, perform high-quality image restoration.
Use Cases
{
"input": "I like this photo, can you remove the raindrops while preserving the content?",
"prompt": "Clear",
"output": "Restored Image"
}
{
"input": "I took this photo on a foggy day, can you improve it?",
"prompt": "Clear",
"output": "Restored Image"
}
{
"input": "Can you remove the small dots in the image? They are quite unpleasant",
"prompt": "Clear",
"output": "Restored Image"
}
Features
Image Denoising
Rain Removal
Defogging
Deblurring
Low-light Image Enhancement
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