Getting sharper night photos by understanding light better

Blurry lights and odd colors in night shots usually come from flare—those unwanted bright spots caused by strong light sources. Most photo-fixing tools ignore how flare mixes with the real light in the scene, so they often leave behind messy patches of wrong colors or fuzzy edges. A new approach tries to fix this by first studying how light actually behaves at night. Instead of just guessing, it uses a smart method called Kolmogorov-Arnold Networks (KANs) to learn the complex patterns of nighttime lighting. This lets the system spot flare more accurately because it knows what normal light should look like. Instead of treating flare as just another blur, it treats it as a mix of different light waves that can be separated and adjusted.The system also learns to adjust itself based on how bright the scene is. If a streetlight is very bright, the tool knows to focus on removing the flare without washing out the rest of the photo. It does this by breaking down the image into different frequencies—like separating high-pitched sounds from low rumbles—and then fine-tuning each part. To train this system properly, the creators didn’t just use fake flare images. They mixed real night scenes with synthetic flare in a smart way, swapping parts of the image to teach the model what’s real and what’s not. This helps the tool learn without getting fooled by unrealistic training data. At its core, the method is about seeing light not as a flat picture but as layers of information. By understanding the deeper patterns in illumination, the system can clean up flare while keeping the actual details of the scene intact. Tests show it works better than older methods at removing glare without leaving behind strange color shifts or soft edges.