The Science of
True Clarity
We are democratizing professional photo restoration. By harnessing the power of advanced proprietary diffusion models, we solve one of photography's oldest and most difficult problems: unwanted reflections.
The Physics of the Problem
When you take a photo through a window, a vitrine, or of a glossy object, your camera sensor captures two distinct signals simultaneously:
- TThe Transmission (T)The actual scene behind the glass that you intended to capture.
- RThe Reflection (R)The light bouncing off the glass surface from your side.
Mathematically, your photo (I) is a linear combination: I = T + R. Most editors treat 'I' as a single flat image. We don't.
Our Proprietary Solution
Beyond Simple Inpainting
Traditional "magic erasers" use a technique called Inpainting. They identify an unwanted object, delete it, and then "guess" what should fill the hole based on surrounding pixels. This works for removing a trash can from a park, but it fails with glare.
Why? Because glare isn't an object blocking the view; it's light added on top of the view. Using inpainting on a face reflected in a window would result in a blurry featureless patch, because the software doesn't know what the skin texture looks like underneath.
Layer Separation Technology
Clarity AI uses a custom-trained Proprietary Diffusion Model designed specifically for signal separation. Instead of guessing, our AI understands the semantic structure of reflections.
When you upload a photo, our inference engine runs a complex reverse-diffusion process. It predicts the probability of every pixel belonging to the "Transmission" layer versus the "Reflection" layer. It then effectively subtracts the 'R' value while preserving the 'T' value.
The Result: Truth, Not Fiction
This approach allows us to recover details that are actually there but hidden by light. We don't hallucinate new eyes or windows; we reveal the ones captured by your sensor. This makes Clarity AI the only choice for:
The Inference Pipeline
Semantic Analysis
The model first identifies the context. Is it a face behind glasses? A cityscape behind a window? This semantic understanding guides the aggressive nature of the removal.
Signal Decoupling
Using our proprietary weights, the model generates a pixel-wise alpha map of the reflection, effectively isolating the high-frequency light interference.
Reconstruction
The final image is reconstructed by enhancing the transmission layer and normalizing contrast, ensuring natural colors and sharp edges.
Automated • Secure • State-of-the-Art