Convert images to pure black and white using customisable threshold values.
Convert images to pure black and white using threshold adjustment for high-contrast binary images
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The Threshold tool converts images into pure black and white (binary) images by comparing each pixel's brightness against a threshold value. Pixels brighter than the threshold become white, while darker pixels become black. This creates high-contrast images perfect for document scanning, OCR preparation, artistic effects, and technical applications.
The threshold process evaluates each pixel's brightness value (0-255) and makes a binary decision. For example, with a threshold of 128:
Grayscale preserves all brightness levels (0-255), showing varying shades of gray. Threshold creates binary images with only two values: pure black (0) and pure white (255). Threshold is more extreme and removes all gray tones, making it ideal for high-contrast applications like document scanning where you need clear separation between text and background.
Your threshold value needs adjustment. If mostly black, your threshold is too high - lower it so more pixels pass the brightness test and become white. If mostly white, your threshold is too low - raise it so fewer pixels qualify as "bright enough" to become white. Try starting at 128 and adjusting in increments of 20-30 until you get better balance.
Use Luminance for 95% of cases as it best matches human vision. Try Simple Average if you want equal treatment of all colors. Use individual color channels when your image has distinct color information you want to emphasize. For example, use Green Channel for vegetation-heavy photos or Red Channel when isolating red-stamped text on documents.
Yes! Threshold is excellent for handwritten notes. Take a photo of your notes, apply threshold with a value around 140-180 (depending on lighting), and you'll get clean black text on white background. This removes shadows, improves contrast, and makes text more readable. It also significantly reduces file size compared to grayscale photos.
The Special Filters Black & White creates a grayscale image with smooth tones from black through gray to white. Threshold creates a binary image with only pure black and pure white - no grays at all. Use Black & White filter for artistic photography, use Threshold for documents, technical work, or when you specifically need high-contrast binary output.
Binary images create hard edges because there are no intermediate gray values to smooth transitions. This "aliasing" or "jaggedness" is normal for threshold conversion. It's actually desirable for documents and text as it creates sharp, crisp edges. For smoother results in artistic work, consider using the High Contrast filter instead, which preserves some tonal gradation.
Yes, threshold is commonly used for laser engraving preparation. Binary images work perfectly with most laser engravers since they operate in on/off mode (burn/don't burn). Experiment with threshold values to get the right amount of detail - typically 110-140 works well for photos, but this depends on your source image and engraver settings.
Uneven lighting (like shadows on one side of a document) can make threshold challenging. No single threshold value will work well across the entire image. Pre-process your image first: use the Brightness & Contrast or Exposure tools to even out lighting, or try the Equalize Image tool to balance brightness before applying threshold. Photography tip: ensure even lighting when capturing images you plan to threshold.
For QR codes and barcodes, use values between 120-140. These codes are designed to be binary (black modules on white background), so threshold works perfectly. If your code photo has good lighting and contrast, 128 is ideal. Adjust slightly if the code has shadows or glare. After thresholding, the code should scan reliably with any QR/barcode reader.
This single-image tool requires individual processing. For batch processing of documents or multiple images with consistent threshold settings, you'll need to process them one at a time. When processing a series of related images (like pages of a document), find the optimal threshold value for the first image, then use that same value for all subsequent images to ensure consistency.
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