// simulate color vision deficiency on any image
Free browser-based color blindness simulator. Upload any image and preview how it appears to people with Deuteranopia, Protanopia, Tritanopia, and 5 other vision types. No sign-up required.
Click or drag & drop an image
PNG, JPG, WebP, GIF — processed locallyUpload an image to preview
Your image never leaves your deviceClick the upload zone or drag and drop any PNG, JPG, or WebP image. It stays in your browser — nothing is uploaded to a server.
Select from 8 color vision conditions. The simulation updates instantly in the right panel.
Use Compare to see original vs. simulated side-by-side, or Grid to see all 8 types at once. Download the simulated result.
A free browser-based tool that simulates how people with color vision deficiencies perceive images. Upload any image and instantly see it through 8 different vision conditions — all processing happens in your browser via Canvas API. No uploads, no tracking.
Deuteranopia is the most common form of red-green color blindness, affecting roughly 1% of males. People with deuteranopia lack functioning M-cones (green-sensitive). Reds, greens, and browns can appear similar — typically shifted toward yellow or orange hues. Deuteranomaly is a milder variant where M-cones are present but shifted, affecting about 5% of males.
Protanopia is another form of red-green color blindness caused by missing L-cones (red-sensitive). Reds appear very dark — almost black — while greens and reds are confused. It affects about 1% of males. Protanomaly is a milder variant where L-cones are present but have a shifted spectral response.
Tritanopia is a rare form of color blindness affecting the S-cones (blue-sensitive), causing blue-yellow confusion. Blues appear greenish and yellows appear violet or light grey. Unlike red-green color blindness, tritanopia is not sex-linked and affects males and females equally. It affects less than 0.01% of the population.
Achromatopsia (complete color blindness) is an extremely rare condition where no functional cone cells are present. Affected individuals see only in shades of grey and typically experience severe light sensitivity (photophobia) and reduced visual acuity. It affects roughly 1 in 30,000 people worldwide.
No. This tool uses the browser's Canvas API to process your image entirely on your device. No image data is ever sent to any server. The file stays in your browser's memory for the duration of the session only.
The simulations use established color transformation matrices based on the Brettel, Viénot and Mollon (1997) and Machado, Oliveira and Velho (2009) research models. They are widely used in professional accessibility tools. While no simulation can perfectly represent subjective visual experience, they provide a scientifically grounded approximation useful for design decisions.
Color blindness (color vision deficiency) is the reduced ability to distinguish between certain colors. It affects approximately 8% of males and 0.5% of females of Northern European descent. The condition arises from absent or malfunctioning cone cells in the retina — the light-sensitive cells responsible for color perception.
Most color blindness is inherited, but it can also result from eye disease, certain medications, or aging. There are several distinct types depending on which cone type is affected, each producing a different pattern of color confusion.
The three main categories correspond to the three types of cone cells: L (long wavelength, red-sensitive), M (medium wavelength, green-sensitive), and S (short wavelength, blue-sensitive).
Approximately 300 million people worldwide live with some form of color vision deficiency. Designing without considering these users risks making your content inaccessible — charts that use red and green to differentiate data, forms where error states rely on color alone, or navigation that uses color as the only indicator all become problematic.
Key design principles for accessible color use include: never use color as the only means of conveying information (pair it with text, icons, or patterns); ensure sufficient contrast between adjacent elements; test UI designs, infographics, and data visualizations through multiple simulation types before release.
This tool transforms each pixel of your image using color transformation matrices derived from clinical research into human color vision. The image is decoded into linear RGB, transformed through a vision-type-specific matrix that models cone response, then re-encoded for display. The matrices used are based on the Brettel–Viénot–Mollon (1997) dichromacy model and the Machado–Oliveira–Velho (2009) anomalous trichromacy model — two of the most cited models in the accessibility research literature.