On the Properties of an Image Smoothing Algorithm for Colored Images Based on Gradient Analysis
DOI:
https://doi.org/10.14529/cmse200301Keywords:
image smoothing, filter, gradientAbstract
In this paper properties and possible applications of an image smoothing algorithm are explored. The algorithm allows us to remove small textures and to preserve main structures on the image. Algorithm is based on the analysis of two gradient components. These components are the length and the angle of gradient vector. Theory that underlies the algorithm is based on distinction between two types of boundaries, which differ in behavior of gradient vectors. We suppose that closeness of gradient angles in given neighborhood means that points belong to the same boundary. This in turn means that they should have bigger weights. We also take into consideration inverted gradient length as the factor for weight computation. Our goal is to focus on the results of applying the algorithm as a preprocessing step for the tasks like edge detection. This method shows interesting results as a preprocessing step for edge detection tasks. It smooths insignificant details, from which we do not need edges to be shown at the image of the edges. We also explore interesting properties of using algorithm for several iterations and its behavior on noise reduction task.
References
Bi S. An L1 Image Transform for Edge-preserving Smoothing and Scene-level Intrinsic Decomposition. ACM Transactions on Graphics. 2015. Vol. 4, no. 34. P. 78–90. DOI: 10.1145/2766946.
Canny J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986. Vol. PAMI-8, no. 6. P. 679–698. DOI: 10.1109/TPAMI.1986.4767851.
Chen Q. Fast Image Processing with Fully-convolutional Networks. 2017 IEEE International Conference on Computer Vision, ICCV (Venice, Italy, October, 22–29, 2017). 2017. P. 2497–2506. DOI: 10.1109/iccv.2017.273.
Gudkov V. Image Smoothing Algorithm Based on Gradient Analysis. Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT (Yekaterinburg, Russia, May, 14–15, 2020). 2020. P. 403–406. DOI: 10.1109/USBEREIT48449.2020.9117646.
Fan Q. Image Smoothing via Unsupervised Learning. ACM Transactions on Graphics 2018. Vol. 37, no. 6. DOI: 10.1145/3272127.3275081.
He K. Guided Image Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013. Vol. 6, no. 35. P. 1397–1409. DOI: 10.1109/TPAMI.2012.213.
Lu K. Deep Texture and Structure Aware Filtering Network for Image Smoothing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018. Vol. 2, no. 11208. P. 229–245. DOI: 10.1007/978-3-030-01225-0_14.
Perona P. Scale-space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1990. Vol. 7, no. 12. P. 629–639. DOI: 10.1109/34.56205.
Tomasi C. Bilateral Filtering for Gray and Color Images. Proceedings of the IEEE International Conference on Computer Vision (Bombay, India, January, 7, 1998). 1998. P. 839–846.
Pham C. Adaptive Guided Image Filtering for Sharpness Enhancement and Noise Reduction. PSIVT 2011. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 2011. Vol. 7087. P. 323–334. DOI: 10.1007/978-3-642-25367-6_29.
Xiaonan F. Learning Explicit Smoothing Kernels for Joint Image. Pacific Graphics. 2019. Vol. 7, no. 38. P. 180–190. DOI: 10.1111/cgf.13827.
Xu L. Image Smoothing via L0 Gradient. ACM Transactions on Graphics. 2011. Vol. 6, no. 30. P. 1–12. DOI: 10.1145/2070781.2024208.
Yoshimura K. Structure-tensor-based Anisotropic Rolling Filter for Image Smoothing. Proceedings of SPIE. 2019. no. 1104904. P. 13–18. DOI: 10.1117/12.2517892.
Zhang Q. Rolling Guidance Filter. Computer Vision-ECCV2014. Springer, Cham. 2014. P. 851–830. DOI: 10.1007/978-3-319-10578-9_53.
