Optimization of Turbulence Image Chromatic Data based on Surface Construction and RANSAC Estimation

For the purpose of meeting the requirement for image chromatic information storage, data processing and transmission in turbulence precise detection, this paper presents a new data optimization method of turbulence image chromatic data based on energy optimization surface construction and multi-order Random Sample Consensus (RANSAC) estimation. Though extracting turbulence image’s chromatic data in color-space, we compute image pixel’s normal vector, multi-order derivative vector and partial derivative vector, thus an energy optimization surface of chromatic data can be structured; subsequently, by expanding the multi-order RANSAC estimation, the multiorder RANSAC illustration of the layered chromatic vector surface can be realized, which contributes to the chromatic data optimization of turbulence image in different dimensional RANSAC estimation levels. Optimization experiment and performance comparison prove that an effective and reliable optimization results of turbulence image chromatic data can be obtained, an efficient method for studying the chromatic data and vector surface of turbulence image in different dimensional estimation level is also presented.