Identify if image shows chromatic aberration using AI

Below is a free classifier to identify if image shows chromatic aberration. Just upload your image, and our AI will predict if the image shows chromatic aberration - in just seconds.

if image shows chromatic aberration identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("if-image-shows-chromatic-aberration", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/if-image-shows-chromatic-aberration/invoke', {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
            'Content-Type': 'application/json',
        },
        body: JSON.stringify(
            {"data": "your_image_url"}
        )
    })
    .then(response => response.json())
    .then(data => console.log(data));
                

    curl -X POST \
        -H "Content-Type: application/json" \
        -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
        -d '{"data": "your_image_url"}' \
        https://www.nyckel.com/v1/functions/if-image-shows-chromatic-aberration/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict if the image shows chromatic aberration.

This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Aberration and Clean.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if the image shows chromatic aberration).

Whether you're just curious or building if image shows chromatic aberration detection into your application, we hope our classifier proves helpful.

Need to identify if image shows chromatic aberration at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Photography Quality Assurance: Photographers can use the chromatic aberration identifier to automatically assess the quality of their images. By flagging pictures with noticeable aberration, they can selectively edit or reject images before final processing, ensuring higher overall quality in their portfolios.

  • Lens Manufacturing Quality Control: Camera and lens manufacturers can incorporate this function into their quality control processes. By testing lenses for chromatic aberration on images they produce, manufacturers can ensure that their products meet quality standards before reaching the market.

  • Image Editing Software Optimization: Image editing software can integrate the chromatic aberration identifier to assist users in correcting image imperfections. By automatically detecting aberrations, the software can recommend specific fixes, streamlining the editing process and improving user efficiency.

  • Digital Asset Management Systems: Digital asset management platforms can utilize the chromatic aberration identifier to help users filter and organize their image collections. By categorizing images based on visual quality, users can more easily manage their assets and prioritize edits on high-quality images.

  • Automated Photography Review and Tagging: Photography platforms can develop automated systems that utilize this identification function to assess user-uploaded images. By tagging images marked with chromatic aberration, the platform can guide users in submitting only high-quality work, encouraging better photographic practices.

  • E-commerce Product Photography: E-commerce businesses can employ the chromatic aberration identifier to ensure that product images meet high visual standards. By screening out images with aberration before they go live on product pages, businesses can enhance customer perception and reduce returns due to misleading image quality.

  • AI Training Datasets Preparation: Researchers and developers creating AI models for image recognition can apply the chromatic aberration identifier to curate training datasets. By filtering out images with aberrations, they can ensure their datasets are composed of high-quality images, leading to improved model performance in various image analysis applications.

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