Star-Effect Simulation for Photography Using Self-calibrated Stereo Vision
Star effects are an important design factor for night photos. Progress in imaging technologies made it possible that night photos can be taken free-hand. For such camera settings, star effects are not achievable. We present a star-effect simulation method based on self-calibrated stereo vision. Given an uncalibrated stereo pair (i.e. a base image and a match image), which can be just two photos taken with a mobile phone with about the same pose, we follow a standard routine: Extract a family of feature-point pairs, calibrate the stereo pair by using the feature-point pairs, and obtain depth information by stereo matching. We detect highlight regions in the base image, estimate the luminance according to available depth information, and, finally, render star patterns with an input texture. Experiments show that our results are similar to real-world star effect photos, and that they are more natural than results of existing commercial applications. The paper reports for the first time research on automatically simulating photo-realistic star effects.
KeywordsStar effect Computational photography Stereo vision Self-calibration
This project is supported by the China Scholarship Council.
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