3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

• 作 者: Bei-Ji Zou Yun-Di Guo Qi He Ping-Bo Ouyang Ke Liu Zai-Liang Chen
• 机 构: School of Information Science and Engineering, Central South University, Changsha 410083, China;Cent
• 出 处: 计算机科学技术学报（英文版）
• 发表日期: 2018年4期
• 关键词: block matching convolutional neural network (CNN) denoising 3D filtering

Block matching based 3D filtering methods have achieved great success in image denoising tasks. However, the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network (CNN) for the 3D filtering step to le a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model (BMCNN) could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio (PSNR) when denoising images with high noise levels (σ＞40), and the best visual quality when denoising images with all the tested noise levels.