Sharpening Method for Dynamic Images of Remote Network Video
School of Computer and Information Science, Hunan Institute of Technology
No.18 Henghua Road, Zhuhui District, Hengyang, Hunan 421001, China
Sharpening for dynamic images of remote network video is helpful to improve the dynamic images quality of remote network video and facilitate the subsequent use. Currently, most of remote network video dynamic images are completed based on the DSP chip, the cost of processing is high. In this paper, we propose a method to sharpen remote network video dynamic images based on the physical model. Firstly, image enhancement is carried out. Then, the dark channel priority method and the transmittance estimation method are analyzed to complete the sharpening. Experiments show that the proposed method can effectively improve the efficiency of image sharpening, and the sharpness of image is high and the practicability is strong.
-  P. Lu, H. Wu, G. Qiao, W. Li, M. Scaioni, T. Feng, S. Liu, W. Chen, N. Li, C. Liu, X. Tong, Y. Hong, and R. Li, “Model test study on monitoring dynamic process of slope failure through spatial sensor network,” Environmental Earth Sciences, Vol.74, No.4, pp. 3315-3332, 2015.
-  G. Yang, W. Tan, H. Jin, T. Zhao, and L. Tu, “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.
-  J. A. Ramirez-Quintana and M. I. Chacon-Murguia, “An Adaptive Unsupervised Neural Network Based on Perceptual Mechanism for Dynamic Object Detection in Videos with Real Scenarios,” Neural Processing Letters, Vol.42, No.3, pp. 665-689, 2015.
-  J. Nakajima and M. West, “Dynamic network signal processing using latent threshold models,” Digital Signal Processing, Vol.47, No.92-C, pp. 5-16, 2015.
-  A. Hagopian, B. Stover, and S. Barnhart, “CDC Clearance Process Constitutes an Obstacle to Progress in Public Health,” American J. of Public Health, Vol.105, No.6, pp. e1, 2015.
-  M. Jung, J. Hwang, and J. Tani, “Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences,” Plos One, Vol.10, No.7, pp. 1-16, 2015.
-  Y. Liu, “Sea Surface Scattering Image Defogging Algorithm Based on Uniformly Ergodic Texture Addressing,” Bulletin of Science and Technology, Vol.31, No.2, pp. 200-202, 2015.
-  X. Wang and M. Ju, “Fast Image Haze Removal Based on Dark Channel Prior,” Science Technology and Engineering, Vol.16, No.20, pp. 66-72, 2016.
-  A. Zhao, Y. Wei, T. Liu, et al., “Edge Detection Algorithm Robinson Based on Histogram Equalization,” Computer Measurement and Control, Vol.24, No.6, pp. 230-232, 2016.
-  L. Su, J. Wu, and Y. Yin, “An Improved Sea Fog Removal Algorithm of Panoramic Image,” Computer Simulation, Vol.33, No.11, pp. 269-273, 2016.
-  Y. Liu and W. Zhong, “A new method of image enhancement about moving target based on rough set theory,” Electronic Design Engineering, Vol.24, No.8, pp. 134-137, 2016.
-  X. Song, H. Zhao, Q. Lu, S. Xia, R. Xi, M. Li, and M. Xiao, “Clearness Processing of Haze-degraded Images,” Telecommunication Engineering, Vol.56, No.2, pp. 208-211, 2016.
-  H. Wang, “Motivation to image restoration algorithm of laser underwater optical imaging research,” Laser J., Vol.37, No.9, pp. 122-125, 2016.
-  B. Wu, H. Fu, and H.-Y. Zhang, “De-hazing of atmosphere veil haze images,” Optics and Precision Engineering, Vol.24, No.8, pp. 2018-2026, 2016.