Histogram Equalization

Apply histogram equalization to enhance image contrast by redistributing pixel intensities.

Request Parameters

Response

Returns the contrast-enhanced image in the same format as the input file.

Example Request

    curl -X POST "https://oyyi.xyz/api/image/histogram-equalization" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@image.jpg" \
  --output equalized.jpg
  

Example with Python

    import requests

url = "https://oyyi.xyz/api/image/histogram-equalization"

files = {
    'file': open('image.jpg', 'rb')
}

response = requests.post(url, files=files)

# Save the equalized image
with open('equalized.jpg', 'wb') as f:
    f.write(response.content)
  

Example with JavaScript

    // Using fetch API
const formData = new FormData();
formData.append('file', fileInput.files[0]);

fetch('https://oyyi.xyz/api/image/histogram-equalization', {
  method: 'POST',
  body: formData
})
.then(response => response.blob())
.then(blob => {
  // Create a URL for the blob
  const url = URL.createObjectURL(blob);

  // Create a link to download the image
  const a = document.createElement('a');
  a.href = url;
  a.download = 'equalized.jpg';
  a.click();

  // Clean up by revoking the URL
  URL.revokeObjectURL(url);
})
.catch(error => console.error('Error:', error));
  

How It Works

Histogram equalization is a technique used to enhance the contrast of an image by redistributing pixel intensity values:

• The algorithm calculates the histogram of the image, which shows the distribution of pixel intensities.
• It then computes a cumulative distribution function (CDF) from the histogram.
• The CDF is normalized and used as a mapping function to transform the original pixel values.
• This process spreads out the most frequent intensity values, effectively increasing the global contrast of the image.

Error Responses

Notes

• Histogram equalization works best on images with poor contrast due to lighting conditions or sensor limitations.
• The technique is particularly effective for scientific images like X-rays, thermal images, or microscopy images.
• For color images, the equalization is applied to the luminance/value channel only, preserving the hue and saturation.
• While histogram equalization enhances global contrast, it may sometimes lead to over-enhancement or unnatural-looking results.
• The maximum file size allowed is 10MB.
• This operation preserves the alpha channel (transparency) if present in the original image.

Use Cases

• Enhancing low-contrast images due to poor lighting conditions
• Improving visibility in foggy or hazy images
• Enhancing medical imaging data like X-rays or MRIs
• Preprocessing images for computer vision applications
• Improving the readability of scanned documents
• Enhancing details in underexposed or overexposed photographs