Color Correction for Stereoscopic Images Based on Gradient Preservation
Color correction can eliminate the color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local color correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent color correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large color difference.
KeywordsSIFT feature match Color correction Region mapping Gradient preservation
The presented research work is supported by the National Natural Science Foundation of China under Grant 61672158, Grant 61671152, and Grant 61502105, in part by the Fujian Natural Science Funds for Distinguished Young Scholar under Grant 2015J06014, in part by the Technology Guidance Project of Fujian Province under Grant No. 2017H0015, and in part by the Fujian Collaborative Innovation Center for Big Data Application in government.
- 1.Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics 21(5), 34–41 (2002)Google Scholar
- 2.Wang, Q., Yan, P., Yuan, Y., Li, X.: Robust color correction in stereo vision. In: IEEE International Conference on Image Processing, pp. 965–968 (2011)Google Scholar
- 3.Pitie, F., Kokaram, A.C., Dahyot, R.: N-dimensional probability density function transfer and its application to colour transfer. In: IEEE International Conference on Computer Vision, pp. 1434–1439 (2005)Google Scholar
- 4.Piti, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Comput Vis. Image Underst. 107(1C2), 123–137 (2007)Google Scholar
- 5.Zheng, X., Niu, Y., Chen, J., Chen, Y.: Color correction for stereo-scopic image based on matching and optimization. In International Conference on 3D Immersion, pp. 1–8 (2017)Google Scholar
- 7.Xiao, X., Ma, L.: Color transfer in correlated color space. In: International Conference on Virtual Reality Continuum, pp. 305–309 (2006)Google Scholar
- 10.Xiao, X., Ma, L.: Gradient-Preserving Color Transfer. Comput. Graphics Forum 28, 1879–1886 (2009). https://doi.org/10.1111/j.1467-8659.2009.01566.x
- 12.Niu, Y., Zhang, H., Guo, W., Ji, R.: Image quality assessment for color correction based on color contrast similarity and color value difference. IEEE Trans. on Circuits Syst. Video Technol. 28(4), 849–862 (2018)Google Scholar
- 21.Chang, H.-W., Zhang, Q.-W., Wu, Q.-G., Gan, Y.: Perceptual image quality assessment by independent feature detector. Neurocomputing 151, 1142–1152, March (2015)Google Scholar