Cui Chengcheng
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044,China；Zhou Xianchun
1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044,China； 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044,China； 3.School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044,ChinaZan Mingyuan
School of Chang Wang, Nanjing University of Information Science and Technology, Nanjing 210044,ChinaChen Jing
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044,China；Wang Zhifei
School of Chang Wang, Nanjing University of Information Science and Technology, Nanjing 210044,ChinaYin Hao
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044,China；1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044,China; 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044,China; 3.School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044,China; 4.School of Chang Wang, Nanjing University of Information Science and Technology, Nanjing 210044,China
TP391.41
Image based denoising algorithm is the basis of building intelligent video surveillance system, which is of great significance to capture things accurately. Considering that the hard threshold of BM3D denoising algorithm cannot adapt to the noise intensity and lacks the protection of image edge texture information, an improved BM3D image denoising algorithm based on adaptive filtering is proposed. Firstly, the adaptive filter is used to replace the hard threshold filter to match the similar blocks in the basic estimation stage. More accurately, soft threshold is applied to the high-noise region and total variation is applied to the low-noise region. Then in the final estimation stage, the K-means clustering method is used to find the matching blocks to obtain the final denoising image. The experimental results show that the new algorithm improves the PSNR of images by an average of 0.89dB, and the SSIM of images by an average of 1.05 times. At the same time, it avoids the edge ringing effect caused by the traditional algorithm, which is beneficial to practical application.
